| Item Type | Journal Article |
|---|---|
| Author | Martin Geroldinger |
| Author | Johan Verbeeck |
| Author | Konstantin E. Thiel |
| Author | Geert Molenberghs |
| Author | Arne C. Bathke |
| Author | Martin Laimer |
| Author | Georg Zimmermann |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.202200236 |
| Volume | n/a |
| Issue | n/a |
| Pages | e2200236 |
| Publication | Biometrical Journal |
| ISSN | 1521-4036 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202200236 |
| DOI | 10.1002/bimj.202200236 |
| Accessed | 3/13/2023, 4:32:11 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Ordinal data in a repeated measures design of a crossover study for rare diseases usually do not allow for the use of standard parametric methods, and hence, nonparametric methods should be considered instead. However, only limited simulation studies in settings with small sample sizes exist. Therefore, starting from an Epidermolysis Bullosa simplex trial with the above-mentioned design, a rank-based approach using the R package nparLD and different generalized pairwise comparisons (GPC) methods were compared impartially in a simulation study. The results revealed that there was not one single best method for this particular design, because a trade-off exists between achieving high power, accounting for period effects, and for missing data. Specifically, nparLD as well as the unmatched GPC approaches do not address crossover aspects, and the univariate GPC variants partly ignore the longitudinal information. The matched GPC approaches, on the other hand, take the crossover effect into account in the sense of incorporating the within-subject association. Overall, the prioritized unmatched GPC method achieved the highest power in the simulation scenarios, although this may be due to the specified prioritization. The rank-based approach yielded good power even at a sample size of N=6$N=6$, whereas the matched GPC method could not control the type I error. |
| Date Added | 3/13/2023, 4:32:11 PM |
| Modified | 3/13/2023, 4:32:35 PM |
| Item Type | Journal Article |
|---|---|
| Author | Christy Cassarly |
| Author | Renee’ H. Martin |
| Author | Marc Chimowitz |
| Author | Edsel A. Peña |
| Author | Viswanathan Ramakrishnan |
| Author | Yuko Y. Palesch |
| URL | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0187050 |
| Volume | 12 |
| Issue | 10 |
| Pages | e0187050 |
| Publication | PLOS ONE |
| ISSN | 1932-6203 |
| Date | Oct 26, 2017 |
| Extra | Publisher: Public Library of Science |
| Journal Abbr | PLOS ONE |
| DOI | 10.1371/journal.pone.0187050 |
| Accessed | 12/16/2022, 7:03:44 AM |
| Library Catalog | PLoS Journals |
| Language | en |
| Abstract | Historically, ordinal measures of functional outcome have been dichotomized for the primary analysis in acute stroke therapy trials. A number of alternative methods to analyze the ordinal scales have been proposed, with an emphasis on maintaining the ordinal structure as much as possible. In addition, despite the availability of longitudinal outcome data in many trials, the primary analysis consists of a single endpoint. Inclusion of information about the course of disease progression allows for a more complete understanding of the treatment effect. Multistate Markov modeling, which allows for the full ordinal scale to be analyzed longitudinally, is compared with previously suggested analytic techniques for the ordinal modified Rankin Scale (dichotomous-logistic regression; continuous-linear regression; ordinal- shift analysis, proportional odds model, partial proportional odds model, adjacent categories logit model; sliding dichotomy; utility weights; repeated measures). In addition, a multistate Markov model utilizing an estimate of the unobservable baseline outcome derived from principal component analysis is compared Each of the methods is used to re-analyze the National Institute of Neurological Diseases and Stroke tissue plasminogen activator study which showed a consistently significant effect of tissue plasminogen activator using a global test of four dichotomized outcomes in the analysis of the primary outcome at 90 days post-stroke in the primary analysis. All methods detected a statistically significant treatment effect except the multistate Markov model without predicted baseline (p = 0.053). This provides support for the use of the estimated baseline in the multistate Markov model since the treatment effect is able to be detected with its inclusion. Multistate Markov modeling allows for a more refined examination of treatment effect and describes the movement between modified Rankin Scale states over time which may provide more clinical insight into the treatment effect. Multistate Markov models are feasible and desirable in describing treatment effect in acute stroke therapy trials. |
| Date Added | 12/16/2022, 7:03:44 AM |
| Modified | 12/16/2022, 7:04:29 AM |
| Item Type | Journal Article |
|---|---|
| Author | Tong-Yu Lu |
| Author | Wai-Yin Poon |
| Author | Siu Hung Cheung |
| URL | https://doi.org/10.1177/0962280211434425 |
| Volume | 24 |
| Issue | 6 |
| Pages | 949-967 |
| Publication | Statistical Methods in Medical Research |
| ISSN | 0962-2802 |
| Date | 2015-12-01 |
| Journal Abbr | Stat Methods Med Res |
| DOI | 10.1177/0962280211434425 |
| Accessed | 8/18/2022, 6:32:19 AM |
| Library Catalog | SAGE Journals |
| Language | en |
| Abstract | Ordered categorical data are frequently encountered in clinical studies. A popular method for comparing the efficacy of treatments is to use logistic regression with the proportional odds assumption. The test statistic is based on the Wilcoxon–Mann–Whitney test. However, the proportional odds assumption may not be appropriate. In such cases, the probability of rejecting the null hypothesis is much inflated even though the treatments have the same mean efficacy. An alternative approach that does not rely on the proportional odds assumption is to conceptualize the responses as manifestations of some underlying continuous variables. However, statistical procedures were developed only for the comparison of two treatments. In this article, we derive testing procedures that compare several treatments to a control, utilizing a latent normal distribution with the latent variable model. The proposed procedure is useful because multiple comparisons with a control is very frequently an objective of a clinical study. Data from clinical trials are used to illustrate the proposed procedures. |
| Date Added | 8/18/2022, 6:32:52 AM |
| Modified | 8/18/2022, 6:33:34 AM |
Took as a “null hypothesis” the equality of means when showing that violation of PO causes elevation of alpha. This represents a misunderstanding of ordinal outcomes.
| Item Type | Journal Article |
|---|---|
| Author | Omidali Aghababaei Jazi |
| Author | Eleanor Pullenayegum |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9417 |
| Volume | n/a |
| Issue | n/a |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2022 |
| DOI | 10.1002/sim.9417 |
| Accessed | 4/26/2022, 7:39:02 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | A common issue in longitudinal studies is that subjects' visits are irregular and may depend on observed outcome values which is known as longitudinal data with informative observation times (follow-up). Semiparametric regression modeling for this type of data has received much attention as it provides more flexibility in studying the association between regression factors and a longitudinal outcome. An important problem here is how to select relevant variables and estimate their coefficients in semiparametric regression models when the number of covariates at baseline is large. The current penalization procedures in semiparametric regression models for longitudinal data do not account for informative observation times. We propose a variable selection procedure that is suitable for the estimation methods based on pseudo-score functions. We investigate the asymptotic properties of penalized estimators and conduct simulation studies to illustrate the theoretical results. We also use the procedure for variable selection in semiparametric regression models for the STAR*D dataset from a multistage randomized clinical trial for treating major depressive disorder. |
| Date Added | 4/26/2022, 10:27:51 AM |
| Modified | 4/26/2022, 10:29:03 AM |
| Item Type | Journal Article |
|---|---|
| Author | Susan C. Scott |
| Author | Mark S. Goldberg |
| Author | Nancy E. Mayo |
| Volume | 50 |
| Pages | 45-55 |
| Publication | J Clin Epi |
| Date | 1997 |
| Extra | Citation Key: sco97sta tex.citeulike-article-id= 13264821 tex.posted-at= 2014-07-14 14:09:42 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 4/17/2022, 9:45:52 AM |
| Item Type | Journal Article |
|---|---|
| Author | Tong Lu |
| Author | Yujie Yang |
| Author | Jin Y. Jin |
| Author | Matts Kågedal |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/psp4.12487 |
| Volume | 9 |
| Issue | 2 |
| Pages | 96-105 |
| Publication | CPT: Pharmacometrics & Systems Pharmacology |
| ISSN | 2163-8306 |
| Date | 2020 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/psp4.12487 |
| DOI | 10.1002/psp4.12487 |
| Accessed | 4/11/2022, 8:38:29 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Longitudinal-ordered categorical data, common in clinical trials, can be effectively analyzed with nonlinear mixed effect models. In this article, we systematically evaluated the performance of three different models in longitudinal muscle spasm adverse event (AE) data obtained from a clinical trial for vismodegib: a proportional odds (PO) model, a discrete-time Markov model, and a continuous-time Markov model. All models developed based on weekly spaced data can reasonably capture the proportion of AE grade over time; however, the PO model overpredicted the transition frequency between grades and the cumulative probability of AEs. The influence of data frequency (daily, weekly, or unevenly spaced) was also investigated. The PO model performance reduced with increased data frequency, and the discrete-time Markov model failed to describe unevenly spaced data, but the continuous-time Markov model performed consistently well. Clinical trial simulations were conducted to illustrate the muscle spasm resolution time profile during the 8-week dose interruption period after 12 weeks of continuous treatment. |
| Short Title | Analysis of Longitudinal-Ordered Categorical Data for Muscle Spasm Adverse Event of Vismodegib |
| Date Added | 4/11/2022, 8:38:29 PM |
| Modified | 4/11/2022, 8:39:03 PM |
| Item Type | Journal Article |
|---|---|
| Author | Nikki van Leeuwen |
| Author | Christa Walgaard |
| Author | Pieter A. van Doorn |
| Author | Bart C. Jacobs |
| Author | Ewout W. Steyerberg |
| Author | Hester F. Lingsma |
| Volume | 14 |
| Issue | 2 |
| Pages | e0211404 |
| Publication | PloS One |
| ISSN | 1932-6203 |
| Date | 2019 |
| Extra | PMID: 30785890 PMCID: PMC6382155 |
| Journal Abbr | PLoS One |
| DOI | 10.1371/journal.pone.0211404 |
| Library Catalog | PubMed |
| Language | eng |
| Abstract | BACKGROUND: Randomized controlled trials (RCTs) pose specific challenges in rare and heterogeneous neurological diseases due to the small numbers of patients and heterogeneity in disease course. Two analytical approaches have been proposed to optimally handle these issues in RCTs: covariate adjustment and ordinal analysis. We investigated the potential gain in efficiency of these approaches in rare and heterogeneous neurological diseases, using Guillain-Barré syndrome (GBS) as an example. METHODS: We analyzed two published GBS trials with primary outcome 'at least one grade improvement' on the GBS disability scale. We estimated the treatment effect using logistic regression models with and without adjustment for prognostic factors. The difference between the unadjusted and adjusted estimates was disentangled in imbalance (random differences in baseline covariates between treatment arms) and stratification (change of the estimate due to covariate adjustment). Second, we applied proportional odds regression, which exploits the ordinal nature of the GBS disability score. The standard error of the estimated treatment effect indicated the statistical efficiency. RESULTS: Both trials were slightly imbalanced with respect to baseline characteristics, which was corrected in the adjusted analysis. Covariate adjustment increased the estimated treatment effect in the two trials by 8% and 18% respectively. Proportional odds analysis resulted in lower standard errors indicating more statistical power. CONCLUSION: Covariate adjustment and proportional odds analysis most efficiently use the available data and ensure balance between the treatment arms to obtain reliable and valid treatment effect estimates. These approaches merit application in future trials in rare and heterogeneous neurological diseases like GBS. |
| Short Title | Efficient design and analysis of randomized controlled trials in rare neurological diseases |
| Date Added | 1/15/2022, 9:24:10 AM |
| Modified | 1/15/2022, 9:24:10 AM |
| Item Type | Journal Article |
|---|---|
| Author | Michael Edlinger |
| Author | Maarten van Smeden |
| Author | Hannes F Alber |
| Author | Maria Wanitschek |
| Author | Ben Van Calster |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9281 |
| Volume | n/a |
| Issue | n/a |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9281 |
| DOI | 10.1002/sim.9281 |
| Accessed | 12/14/2021, 8:11:56 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Calibration is a vital aspect of the performance of risk prediction models, but research in the context of ordinal outcomes is scarce. This study compared calibration measures for risk models predicting a discrete ordinal outcome, and investigated the impact of the proportional odds assumption on calibration and overfitting. We studied the multinomial, cumulative, adjacent category, continuation ratio, and stereotype logit/logistic models. To assess calibration, we investigated calibration intercepts and slopes, calibration plots, and the estimated calibration index. Using large sample simulations, we studied the performance of models for risk estimation under various conditions, assuming that the true model has either a multinomial logistic form or a cumulative logit proportional odds form. Small sample simulations were used to compare the tendency for overfitting between models. As a case study, we developed models to diagnose the degree of coronary artery disease (five categories) in symptomatic patients. When the true model was multinomial logistic, proportional odds models often yielded poor risk estimates, with calibration slopes deviating considerably from unity even on large model development datasets. The stereotype logistic model improved the calibration slope, but still provided biased risk estimates for individual patients. When the true model had a cumulative logit proportional odds form, multinomial logistic regression provided biased risk estimates, although these biases were modest. Nonproportional odds models require more parameters to be estimated from the data, and hence suffered more from overfitting. Despite larger sample size requirements, we generally recommend multinomial logistic regression for risk prediction modeling of discrete ordinal outcomes. |
| Short Title | Risk prediction models for discrete ordinal outcomes |
| Date Added | 12/14/2021, 8:11:56 AM |
| Modified | 12/14/2021, 8:12:37 AM |
| Item Type | Web Page |
|---|---|
| URL | https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(20)30328-3/fulltext |
| Accessed | 11/11/2021, 10:46:04 AM |
| Date Added | 11/11/2021, 10:46:04 AM |
| Modified | 11/11/2021, 10:47:05 AM |
| Item Type | Journal Article |
|---|---|
| Author | David J. Lunn |
| Author | Jon Wakefield |
| Author | Amy Racine-Poon |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.922 |
| Volume | 20 |
| Issue | 15 |
| Pages | 2261-2285 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2001 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.922 |
| DOI | 10.1002/sim.922 |
| Accessed | 10/21/2021, 9:08:27 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Ordered categorical data arise in numerous settings, a common example being pain scores in analgesic trials. The modelling of such data is intrinsically more difficult than the modelling of continuous data due to the constraints on the underlying probabilities and the reduced amount of information that discrete outcomes contain. In this paper we discuss the class of cumulative logit models, which provide a natural framework for ordinal data analysis. We show how viewing the categorical outcome as the discretization of an underlying continuous response allows a natural interpretation of model parameters. We also show how covariates are incorporated into the model and how various types of correlation among repeated measures on the same individual may be accounted for. The models are illustrated using longitudinal allergy data consisting of sneezing scores measured on a four-point scale. Our approach throughout is Bayesian and we present a range of simple diagnostics to aid model building. Copyright © 2001 John Wiley & Sons, Ltd. |
| Short Title | Cumulative logit models for ordinal data |
| Date Added | 10/21/2021, 9:08:27 AM |
| Modified | 10/21/2021, 9:11:10 AM |
Has an example where variance of random effects is greatly reduced when modeling serial dependence using a Markov model vs. using the ordinary random effects model, stating that within-subject variation is mostly explained by serial correlation.
| Item Type | Journal Article |
|---|---|
| Author | Dean Follmann |
| Author | Michael P. Fay |
| Author | Toshimitsu Hamasaki |
| Author | Scott Evans |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8431 |
| Rights | © 2019 John Wiley & Sons, Ltd. |
| Volume | n/a |
| Issue | n/a |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2020 |
| DOI | 10.1002/sim.8431 |
| Accessed | 12/20/2019, 7:01:01 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Composite endpoints are frequently used in clinical trials, but simple approaches, such as the time to first event, do not reflect any ordering among the endpoints. However, some endpoints, such as mortality, are worse than others. A variety of procedures have been proposed to reflect the severity of the individual endpoints such as pairwise ranking approaches, the win ratio, and the desirability of outcome ranking. When patients have different lengths of follow-up, however, ranking can be difficult and proposed methods do not naturally lead to regression approaches and require specialized software. This paper defines an ordering score O to operationalize the patient ranking implied by hierarchical endpoints. We show how differential right censoring of follow-up corresponds to multiple interval censoring of the ordering score allowing standard software for survival models to be used to calculate the nonparametric maximum likelihood estimators (NPMLEs) of different measures. Additionally, if one assumes that the ordering score is transformable to an exponential random variable, a semiparametric regression is obtained, which is equivalent to the proportional hazards model subject to multiple interval censoring. Standard software can be used for estimation. We show that the NPMLE can be poorly behaved compared to the simple estimators in staggered entry trials. We also show that the semiparametric estimator can be more efficient than simple estimators and explore how standard Cox regression maneuvers can be used to assess model fit, allow for flexible generalizations, and assess interactions of covariates with treatment. We analyze a trial of short versus long-term antiplatelet therapy using our methods. |
| Date Added | 12/20/2019, 7:01:01 AM |
| Modified | 10/3/2021, 6:28:11 PM |
| Item Type | Journal Article |
|---|---|
| Author | Michael J. Wurm |
| Author | Paul J. Rathouz |
| Author | Bret M. Hanlon |
| URL | https://www.jstatsoft.org/index.php/jss/article/view/v099i06 |
| Rights | Copyright (c) 2021 Michael J. Wurm, Paul J. Rathouz, Bret M. Hanlon |
| Volume | 99 |
| Issue | 1 |
| Pages | 1-42 |
| Publication | Journal of Statistical Software |
| ISSN | 1548-7660 |
| Date | 2021-09-08 |
| Extra | Number: 1 |
| DOI | 10.18637/jss.v099.i06 |
| Accessed | 9/8/2021, 11:43:02 AM |
| Library Catalog | www.jstatsoft.org |
| Language | en |
| Abstract | Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection. Ordinal regression models are widely used in applications where the use of regularization could be beneficial; however, these models are not included in many popular software packages for regularized regression. We propose a coordinate descent algorithm to fit a broad class of ordinal regression models with an elastic net penalty. Furthermore, we demonstrate that each model in this class generalizes to a more flexible form, that can be used to model either ordered or unordered categorical response data. We call this the elementwise link multinomial-ordinal class, and it includes widely used models such as multinomial logistic regression (which also has an ordinal form) and ordinal logistic regression (which also has an unordered multinomial form). We introduce an elastic net penalty class that applies to either model form, and additionally, this penalty can be used to shrink a non-ordinal model toward its ordinal counterpart. Finally, we introduce the R package ordinalNet, which implements the algorithm for this model class. |
| Date Added | 9/8/2021, 11:43:02 AM |
| Modified | 9/8/2021, 11:43:43 AM |
| Item Type | Journal Article |
|---|---|
| Author | Janie Coulombe |
| Author | Erica E. M. Moodie |
| Author | Robert W. Platt |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9151 |
| Volume | n/a |
| Issue | n/a |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9151 |
| DOI | 10.1002/sim.9151 |
| Accessed | 8/3/2021, 10:43:13 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | In the statistical literature, a number of methods have been proposed to ensure valid inference about marginal effects of variables on a longitudinal outcome in settings with irregular monitoring times. However, the potential biases due to covariate-driven monitoring times and confounding have rarely been considered simultaneously, and never in a setting with an ordinal outcome and a continuous exposure. In this work, we propose and demonstrate a methodology for causal inference in such a setting, relying on a proportional odds model to study the effect of the exposure on the outcome. Irregular observation times are considered via a proportional rate model, and a generalization of inverse probability of treatment weights is used to account for the continuous exposure. We motivate our methodology by the estimation of the marginal (causal) effect of the time spent on video or computer games on suicide attempts in the Add Health study, a longitudinal study in the United States. Although in the Add Health data, observation times are prespecified, our proposed approach is applicable even in more general settings such as when analyzing data from electronic health records where observations are highly irregular. In simulation studies, we let observation times vary across individuals and demonstrate that not accounting for biasing imbalances due to the monitoring and the exposure schemes can bias the estimate for the marginal odds ratio of exposure. |
| Date Added | 8/3/2021, 10:43:13 AM |
| Modified | 8/3/2021, 10:46:11 AM |
| Item Type | Journal Article |
|---|---|
| Author | Trung Dung Tran |
| Author | Emmanuel Lesaffre |
| Author | Geert Verbeke |
| Author | Joke Duyck |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13292 |
| Rights | © 2020 The International Biometric Society |
| Volume | 77 |
| Issue | 2 |
| Pages | 689-701 |
| Publication | Biometrics |
| ISSN | 1541-0420 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13292 |
| DOI | https://doi.org/10.1111/biom.13292 |
| Accessed | 6/22/2021, 8:00:21 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | We propose a Bayesian latent Ornstein-Uhlenbeck (OU) model to analyze unbalanced longitudinal data of binary and ordinal variables, which are manifestations of fewer continuous latent variables. We focus on the evolution of such latent variables when they continuously change over time. Existing approaches are limited to data collected at regular time intervals. Our proposal makes use of an OU process for the latent variables to overcome this limitation. We show that assuming real eigenvalues for the drift matrix of the OU process, as is frequently done in practice, can lead to biased estimates and/or misleading inference when the true process is oscillating. In contrast, our proposal allows for both real and complex eigenvalues. We illustrate our proposed model with a motivating dataset, containing patients with amyotrophic lateral sclerosis disease. We were interested in how bulbar, cervical, and lumbar functions evolve over time. |
| Date Added | 6/22/2021, 8:00:21 AM |
| Modified | 6/22/2021, 8:04:38 AM |
| Item Type | Journal Article |
|---|---|
| Author | Gavin D. Perkins |
| Author | Chen Ji |
| Author | Charles D. Deakin |
| Author | Tom Quinn |
| Author | Jerry P. Nolan |
| Author | Charlotte Scomparin |
| Author | Scott Regan |
| Author | John Long |
| Author | Anne Slowther |
| Author | Helen Pocock |
| Author | John J.M. Black |
| Author | Fionna Moore |
| Author | Rachael T. Fothergill |
| Author | Nigel Rees |
| Author | Lyndsey O’Shea |
| Author | Mark Docherty |
| Author | Imogen Gunson |
| Author | Kyee Han |
| Author | Karl Charlton |
| Author | Judith Finn |
| Author | Stavros Petrou |
| Author | Nigel Stallard |
| Author | Simon Gates |
| Author | Ranjit Lall |
| URL | https://doi.org/10.1056/NEJMoa1806842 |
| Volume | 379 |
| Issue | 8 |
| Pages | 711-721 |
| Publication | New England Journal of Medicine |
| ISSN | 0028-4793 |
| Date | August 23, 2018 |
| Extra | Publisher: Massachusetts Medical Society _eprint: https://doi.org/10.1056/NEJMoa1806842 PMID: 30021076 |
| DOI | 10.1056/NEJMoa1806842 |
| Accessed | 5/22/2021, 9:17:19 AM |
| Library Catalog | Taylor and Francis+NEJM |
| Abstract | Epinephrine in Cardiac Arrest In a randomized trial involving 8014 patients with out-of-hospital cardiac arrest, the use of epinephrine resulted in a significantly higher rate of 30-day survival than placebo but not a higher rate of survival with a favorable neurologic outcome. |
| Date Added | 5/22/2021, 9:17:19 AM |
| Modified | 5/22/2021, 9:17:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Michael P. Fay |
| Author | Erica H. Brittain |
| Author | Joanna H. Shih |
| Author | Dean A. Follmann |
| Author | Erin E. Gabriel |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7799 |
| Rights | Published 2018. This article is a U.S. Government work and is in the public domain in the USA. |
| Volume | 37 |
| Issue | 20 |
| Pages | 2923-2937 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2018 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.7799 |
| DOI | https://doi.org/10.1002/sim.7799 |
| Accessed | 3/31/2021, 10:23:56 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Although the P value from a Wilcoxon-Mann-Whitney test is used often with randomized experiments, it is rarely accompanied with a causal effect estimate and its confidence interval. The natural parameter for the Wilcoxon-Mann-Whitney test is the Mann-Whitney parameter, ϕ, which measures the probability that a randomly selected individual in the treatment arm will have a larger response than a randomly selected individual in the control arm (plus an adjustment for ties). We show that the Mann-Whitney parameter may be framed as a causal parameter and show that it is not equal to a closely related and nonidentifiable causal effect, ψ, the probability that a randomly selected individual will have a larger response under treatment than under control (plus an adjustment for ties). We review the paradox, first expressed by Hand, that the ψ parameter may imply that the treatment is worse (or better) than control, while the Mann-Whitney parameter shows the opposite. Unlike the Mann-Whitney parameter, ψ is nonidentifiable from a randomized experiment. We review some nonparametric assumptions that rule out Hand's paradox through bounds on ψ and use bootstrap methods to make inferences on those bounds. We explore the relationship of the proportional odds parameter to Hand's paradox, showing that the paradox may occur for proportional odds parameters between 1/9 and 9. Thus, large effects are needed to ensure that if treatment appears better by the Mann-Whitney parameter, then treatment improves responses in most individuals. We demonstrate these issues using a vaccine trial. |
| Date Added | 3/31/2021, 10:23:56 PM |
| Modified | 3/31/2021, 10:24:55 PM |
Contrasts between-patient and within-patient treatment effects. In the case of no ties in Y, Eq. (9) provides the exact relationship between the OR and the concordance probability. Need to see if this relationship works only for the true parameters are also for parameter estimates. In Eq. (9) one can show that log phi is almost exactly linear in the log odds ratio, and the slope of predicting log(OR) from logit phi is 0.6974 with a reciprocal of 1.43 which is about the right constant for relating a probit model effect to the concordance probability (perhaps by coincidence). But this differs from the slope of 1.52 that has been empirically found to work in general.
| Item Type | Journal Article |
|---|---|
| Author | REMAP-CAP Investigators |
| URL | https://dx.doi.org/10.1056/nejmoa2100433 |
| Publication | New England Journal of Medicine |
| ISSN | 0028-4793 |
| Date | 2021 |
| Extra | Publisher: Massachusetts Medical Society |
| Journal Abbr | New England Journal of Medicine |
| DOI | 10.1056/nejmoa2100433 |
| Date Added | 2/27/2021, 7:28:40 AM |
| Modified | 2/27/2021, 7:30:16 AM |
| Item Type | Journal Article |
|---|---|
| Author | Isabelle L. Smith |
| Author | Jane E. Nixon |
| Author | Linda Sharples |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8882 |
| Rights | © 2021 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. |
| Volume | n/a |
| Issue | n/a |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.8882 |
| DOI | https://doi.org/10.1002/sim.8882 |
| Accessed | 2/12/2021, 11:20:57 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | For clinical trials where participants pass through a number of discrete health states resulting in longitudinal measures over time, there are several potential primary estimands for the treatment effect. Incidence or time to a particular health state are commonly used outcomes but the choice of health state may not be obvious and these estimands do not make full use of the longitudinal assessments. Multistate models have been developed for some diseases and conditions with the purpose of understanding their natural history and have been used for secondary analysis to understand mechanisms of action of treatments. There is little published on the use of multistate models as the primary analysis method and potential implications on design features, such as assessment schedules. We illustrate methods via analysis of data from a motivating example; a Phase III clinical trial of pressure ulcer prevention strategies. We clarify some of the possible estimands that might be considered and we show, via a simulation study, that under some circumstances the sample size could be reduced by half using a multistate model based analysis, without adversely affecting the power of the trial. |
| Date Added | 2/12/2021, 11:20:57 AM |
| Modified | 2/25/2021, 8:34:44 AM |
| Item Type | Journal Article |
|---|---|
| Author | Val Gebski |
| Author | Karen Byth |
| Author | Rebecca Asher |
| Author | Ian Marschner |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2057 |
| Rights | © 2020 John Wiley & Sons Ltd |
| Volume | 20 |
| Issue | 1 |
| Pages | 77-92 |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2057 |
| DOI | https://doi.org/10.1002/pst.2057 |
| Accessed | 1/30/2021, 3:02:43 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | A model to accommodate time-to-event ordinal outcomes was proposed by Berridge and Whitehead. Very few studies have adopted this approach, despite its appeal in incorporating several ordered categories of event outcome. More recently, there has been increased interest in utilizing recurrent events to analyze practical endpoints in the study of disease history and to help quantify the changing pattern of disease over time. For example, in studies of heart failure, the analysis of a single fatal event no longer provides sufficient clinical information to manage the disease. Similarly, the grade/frequency/severity of adverse events may be more important than simply prolonged survival in studies of toxic therapies in oncology. We propose an extension of the ordinal time-to-event model to allow for multiple/recurrent events in the case of marginal models (where all subjects are at risk for each recurrence, irrespective of whether they have experienced previous recurrences) and conditional models (subjects are at risk of a recurrence only if they have experienced a previous recurrence). These models rely on marginal and conditional estimates of the instantaneous baseline hazard and provide estimates of the probabilities of an event of each severity for each recurrence over time. We outline how confidence intervals for these probabilities can be constructed and illustrate how to fit these models and provide examples of the methods, together with an interpretation of the results. |
| Date Added | 1/30/2021, 3:02:43 PM |
| Modified | 1/30/2021, 3:03:35 PM |
Extension of Berridge and Whitehead
| Item Type | Journal Article |
|---|---|
| Author | Noorian, Sajad |
| Author | Ganjali, Mojtaba |
| URL | https://www.academia.edu/30796618/Bayesian_Analysis_of_Transition_Model_for_Longitudinal_Ordinal_Response_Data_Application_to_Insomnia_Data |
| Volume | 1 |
| Issue | 2 |
| Pages | 148-161 |
| Publication | International Journal of Statistics in Medical Research |
| ISSN | 1929-6029 |
| Date | 2012 |
| Accessed | 12/22/2020, 8:21:14 AM |
| Library Catalog | www.academia.edu |
| Language | en |
| Abstract | Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data: Application to Insomnia Data |
| Short Title | Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data |
| Date Added | 12/22/2020, 8:21:14 AM |
| Modified | 1/24/2021, 7:21:47 AM |
Bayesian inference for Goodman-Kruskal gamma rank correlation using multinomial distribution and Dirichlet prior. Markov proportional odds model with priors for intercepts that are ordered t distribution variates. Also uses a sequential-conditioning Markov-like prior for the coefficients of previous states. Methods don't scale to high number of Y levels. Dataset used is not a very good one as it categorized an ordinal measurement into a very crude ordinal measurement. Transition model is categorical in previous Y level.
| Item Type | Journal Article |
|---|---|
| Author | Fei Yu |
| Author | Hal Morgenstern |
| Author | Eric Hurwitz |
| Author | Thomas R Berlin |
| URL | https://doi.org/10.1191/0962280203sm321ra |
| Volume | 12 |
| Issue | 4 |
| Pages | 321-331 |
| Publication | Statistical Methods in Medical Research |
| ISSN | 0962-2802 |
| Date | August 1, 2003 |
| Extra | Publisher: SAGE Publications Ltd STM |
| Journal Abbr | Stat Methods Med Res |
| DOI | 10.1191/0962280203sm321ra |
| Accessed | 1/3/2021, 9:06:24 AM |
| Library Catalog | SAGE Journals |
| Language | en |
| Abstract | In a randomized clinical trial to assess the effectiveness of different strategies for treating low-back pain in a managed-care setting, 681 adult patients presenting with low-back pain were randomized to four treatment groups: medical care with and without physical therapy; and chiropractic care with and without physical modalities. Follow-up information was obtained by questionnaires at two and six weeks, six, 12 and 18 months and by a telephone interview at four weeks. One outcome measurement at each follow-up is the patient’s self-report on the perception of low-back pain improvement from the previous survey, recorded as ‘A lot better,’ ‘A little better,’ ‘About the same’ and ‘Worse.’ Since the patient’s perception of improvement may be influenced by past experience, the outcome is analysed using a transition (first-order Markov) model. Although one could collapse categories to the point that logistic regression analysis with repeated measurements could be used, here we allow for multiple categories by relating transition probabilities to covariates and previous outcomes through a polytomous logistic regression model with Markov structure. This approach allows us to assess not only the effects of treatment assignment and baseline characteristics but also the effects of past outcomes in analysing longitudinal categorical data. |
| Date Added | 1/3/2021, 9:06:25 AM |
| Modified | 1/22/2021, 4:56:08 PM |
Polytomous Markov logistic model; made no use of the ordinal nature of Y. Shows how to use Cox or Poisson regression to do polytomous regression.
| Item Type | Journal Article |
|---|---|
| Author | Paul S. Albert |
| URL | http://www.jstor.org/stable/2533196 |
| Volume | 50 |
| Issue | 1 |
| Pages | 51-60 |
| Publication | Biometrics |
| ISSN | 0006-341X |
| Date | 1994 |
| Extra | Publisher: [Wiley, International Biometric Society] |
| DOI | 10.2307/2533196 |
| Accessed | 1/3/2021, 9:28:18 AM |
| Library Catalog | JSTOR |
| Abstract | Many chronic diseases follow a course with multiple relapses into periods with severe symptoms alternating with periods of remission; experimental allergic encephalomyelitis, the animal model for multiple sclerosis, is an example of such a disease. A finite Markov chain is proposed as a model for analyzing sequences of ordinal data from a relapsing-remitting disease. The proposed model is one in which the state space is expanded to include information about the relapsing-remitting status as well as the ordinal severity score, and a reparameterization is suggested that reduces the number of parameters needed to be estimated. The Markov model allows for a wide range of relapsing-remitting behavior, provides an understanding of the stochastic nature of the disease process, and allows for efficient estimation of important characteristics of the disease course (such as mean first passage times, occupation times, and steady-state probabilities). These methods are applied to data from a study of the effect of a treatment (transforming growth factor-β1) on experimental allergic encephalomyelitis. |
| Date Added | 1/3/2021, 9:28:18 AM |
| Modified | 1/3/2021, 9:28:52 AM |
Joint model for relapse and disease severity. Not clear why this needs to be two models. For equally spaced measurements.
| Item Type | Journal Article |
|---|---|
| Author | J. K. Lindsey |
| Author | B. Jones |
| Author | A. F. Ebbutt |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291097-0258%2819971230%2916%3A24%3C2873%3A%3AAID-SIM675%3E3.0.CO%3B2-D |
| Rights | Copyright © 1997 John Wiley & Sons, Ltd. |
| Volume | 16 |
| Issue | 24 |
| Pages | 2873-2882 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 1997 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/%28SICI%291097-0258%2819971230%2916%3A24%3C2873%3A%3AAID-SIM675%3E3.0.CO%3B2-D |
| DOI | https://doi.org/10.1002/(SICI)1097-0258(19971230)16:24<2873::AID-SIM675>3.0.CO;2-D |
| Accessed | 1/3/2021, 9:22:28 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | In contrast to other models for ordinal data, the continuation ratio model can be fitted with standard statistical software. This makes it particularly appropriate for large clinical trials with ordinal response variables. In addition, when the trials are longitudinal, this model can be applied to individual responses instead of frequencies in contingency tables. Dependence can be incorporated by conditioning on the previous response, yielding a form of Markov chain. This approach is applied to the analysis of a large seasonal rhinitis trial, where patients were observed over 28 days and six symptoms recorded as ordinal responses. © 1997 John Wiley & Sons, Ltd. |
| Date Added | 1/3/2021, 9:22:28 AM |
| Modified | 1/3/2021, 9:22:59 AM |
Uses continuation ratio model, which requires stringing ot the data doubly (over categories and over time) but is flexible. Does not cover irregular times. States that if an observation is missing in the middle it will require ignoring the next non-missing observation.
| Item Type | Journal Article |
|---|---|
| Author | Kathryn Bartimote-Aufflick |
| Author | Peter C. Thomson |
| URL | https://doi.org/10.1080/02664763.2010.529885 |
| Volume | 38 |
| Issue | 9 |
| Pages | 1883-1897 |
| Publication | Journal of Applied Statistics |
| ISSN | 0266-4763 |
| Date | September 1, 2011 |
| Extra | Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/02664763.2010.529885 |
| DOI | 10.1080/02664763.2010.529885 |
| Accessed | 1/3/2021, 8:47:54 AM |
| Library Catalog | Taylor and Francis+NEJM |
| Abstract | While standard techniques are available for the analysis of time-series (longitudinal) data, and for ordinal (rating) data, not much is available for the combination of the two, at least in a readily-usable form. However, this data type is common place in the natural and health sciences where repeated ratings are recorded on the same subject. To analyse these data, this paper considers a transition (Markov) model where the rating of a subject at one time depends explicitly on the observed rating at the previous point of time by incorporating the previous rating as a predictor variable. Complications arise with adequate handling of data at the first observation (t=1), as there is no prior observation to use as a predictor. To overcome this, it is postulated the existence of a rating at time t=0; however it is treated as ‘missing data’ and the expectation–maximisation algorithm used to accommodate this. The particular benefits of this method are shown for shorter time series. |
| Date Added | 1/3/2021, 8:47:54 AM |
| Modified | 1/3/2021, 8:48:25 AM |
Imputation and E-M for handling missing first observation; does not cover other missings or irregular time points
Emphasis of paper is on the case where the first observation of Y is a response, and one wants to impute the time zero state.
Points to Diggle et al 2nd edition 2002 as primary reference for Markov ordinal models.
Discusses interacting covariates with previous state but doesn't use that in their examples.
| Item Type | Journal Article |
|---|---|
| Author | Cristiano Varin |
| Author | Claudia Czado |
| URL | https://doi.org/10.1093/biostatistics/kxp042 |
| Volume | 11 |
| Issue | 1 |
| Pages | 127-138 |
| Publication | Biostatistics |
| ISSN | 1465-4644 |
| Date | January 1, 2010 |
| Journal Abbr | Biostatistics |
| DOI | 10.1093/biostatistics/kxp042 |
| Accessed | 12/22/2020, 8:37:28 AM |
| Library Catalog | Silverchair |
| Abstract | Longitudinal data with binary and ordinal outcomes routinely appear in medical applications. Existing methods are typically designed to deal with short measurement series. In contrast, modern longitudinal data can result in large numbers of subject-specific serial observations. In this framework, we consider multivariate probit models with random effects to capture heterogeneity and autoregressive terms for describing the serial dependence. Since likelihood inference for the proposed class of models is computationally burdensome because of high-dimensional intractable integrals, a pseudolikelihood approach is followed. The methodology is motivated by the analysis of a large longitudinal study on the determinants of migraine severity. |
| Date Added | 12/22/2020, 8:37:28 AM |
| Modified | 12/22/2020, 8:38:02 AM |
| Item Type | Journal Article |
|---|---|
| Author | I. Lara |
| Author | John Hinde |
| Author | Ariane Castro |
| Author | Iran Silva |
| Pages | 1-16 |
| Publication | Journal of Applied Statistics |
| Date | June 2, 2016 |
| Journal Abbr | Journal of Applied Statistics |
| DOI | 10.1080/02664763.2016.1191623 |
| Library Catalog | ResearchGate |
| Abstract | Categorical data are quite common in many fields of science including in behaviour studies in animal science. In this article, the data concern the degree of lesions in pigs, related to the behaviour of these animals. The experimental design corresponded to two levels of environmental enrichment and four levels of genetic lineages in a completely randomized factorial with data collected longitudinally over four time occasions. The transition models used for the data analysis are based on stochastic processes and Generalized Linear Models. In general, these are not used for analysis of longitudinal data but they are useful in many situations as in this study. We present some aspects of this class of models for the stationary case. The proportional odds transition model is used to construct the matrix of transition probabilities and a function was developed in the R system to fit this model. The likelihood ratio test was used to verify the assumption of odds ratio proportionality and to select the structure of the linear predictor. The methodology used allowed for the choice of a model that can be used to explain the relationship between the severity of lesions in pigs and the use of the environmental enrichment. |
| Date Added | 12/22/2020, 8:32:55 AM |
| Modified | 12/22/2020, 8:33:13 AM |
| Item Type | Journal Article |
|---|---|
| Author | F. Chaubert |
| Author | F. Mortier |
| Author | L. Saint André |
| URL | http://www.sciencedirect.com/science/article/pii/S0047259X08000237 |
| Volume | 99 |
| Issue | 8 |
| Pages | 1717-1732 |
| Publication | Journal of Multivariate Analysis |
| ISSN | 0047-259X |
| Date | September 1, 2008 |
| Journal Abbr | Journal of Multivariate Analysis |
| DOI | 10.1016/j.jmva.2008.01.011 |
| Accessed | 12/22/2020, 8:26:15 AM |
| Library Catalog | ScienceDirect |
| Language | en |
| Abstract | Individual or stand-level biomass is not easy to measure. The current methods employed, based on cutting down a representative sample of plantations, make it possible to assess the biomasses for various compartments (bark, dead branches, leaves, …). However, this felling makes individual longitudinal follow-up impossible. In this context, we propose a method to evaluate individual biomasses by compartments when these are ordinals. Biomass is measured visually and observations are therefore not destructive. The technique is based on a probit model redefined in terms of latent variables. A generalization of the univariate case to the multivariate case is then natural and takes into account of dependency between compartment biomasses. These models are then extended to the longitudinal case by developing a Dynamic Multivariate Ordinal Probit Model. The performance of the MCMC algorithm used for the estimation is illustrated by means of simulations built from known biomass models. The quality of the estimates and the impact of certain parameters, are then discussed. |
| Date Added | 12/22/2020, 8:26:15 AM |
| Modified | 12/22/2020, 8:26:37 AM |
| Item Type | Journal Article |
|---|---|
| Author | M. Mandel |
| Author | R. Betensky |
| Publication | Biostatistics |
| Date | 2008 |
| DOI | 10.1093/biostatistics/kxn008 |
| Library Catalog | Semantic Scholar |
| Abstract | Longitudinal ordinal data are common in many scientific studies, including those of multiple sclerosis (MS), and are frequently modeled using Markov dependency. Several authors have proposed random-effects Markov models to account for heterogeneity in the population. In this paper, we go one step further and study prediction based on random-effects Markov models. In particular, we show how to calculate the probabilities of future events and confidence intervals for those probabilities, given observed data on the ordinal outcome and a set of covariates, and how to update them over time. We discuss the usefulness of depicting these probabilities for visualization and interpretation of model results and illustrate our method using data from a phase III clinical trial that evaluated the utility of interferon beta-1a (trademark Avonex) to MS patients of type relapsing-remitting. |
| Short Title | Estimating time-to-event from longitudinal ordinal data using random-effects Markov models |
| Date Added | 12/22/2020, 8:16:15 AM |
| Modified | 12/22/2020, 8:17:19 AM |
| Item Type | Journal Article |
|---|---|
| Author | Cristiano Varin |
| Author | Claudia Czado |
| URL | https://doi.org/10.1093/biostatistics/kxp042 |
| Volume | 11 |
| Issue | 1 |
| Pages | 127-138 |
| Publication | Biostatistics |
| ISSN | 1465-4644 |
| Date | January 1, 2010 |
| Journal Abbr | Biostatistics |
| DOI | 10.1093/biostatistics/kxp042 |
| Accessed | 12/22/2020, 8:07:13 AM |
| Library Catalog | Silverchair |
| Abstract | Longitudinal data with binary and ordinal outcomes routinely appear in medical applications. Existing methods are typically designed to deal with short measurement series. In contrast, modern longitudinal data can result in large numbers of subject-specific serial observations. In this framework, we consider multivariate probit models with random effects to capture heterogeneity and autoregressive terms for describing the serial dependence. Since likelihood inference for the proposed class of models is computationally burdensome because of high-dimensional intractable integrals, a pseudolikelihood approach is followed. The methodology is motivated by the analysis of a large longitudinal study on the determinants of migraine severity. |
| Date Added | 12/22/2020, 8:07:13 AM |
| Modified | 12/22/2020, 8:08:58 AM |
| Item Type | Journal Article |
|---|---|
| Author | Fiona Steele |
| Author | Emily Grundy |
| URL | https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssc.12446 |
| Rights | © 2020 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society |
| Volume | n/a |
| Issue | n/a |
| Publication | Journal of the Royal Statistical Society: Series C (Applied Statistics) |
| ISSN | 1467-9876 |
| Date | 2020 |
| Extra | _eprint: https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/rssc.12446 |
| DOI | https://doi.org/10.1111/rssc.12446 |
| Accessed | 11/22/2020, 8:31:16 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Exchanges of practical or financial help between people living in different households are a major component of intergenerational exchanges within families and an increasingly important source of support for individuals in need. Using longitudinal data, bivariate dynamic panel models can be applied to study the effects of changes in individual circumstances on help given to and received from non-coresident parents and the reciprocity of exchanges. However, the use of a rotating module for collection of data on exchanges leads to data where the response measurements are unequally spaced and taken less frequently than for the time-varying covariates. Existing approaches to this problem focus on fixed effects linear models for univariate continuous responses. We propose a random effects estimator for a family of dynamic panel models that can handle continuous, binary or ordinal multivariate responses. The performance of the estimator is assessed in a simulation study. A bivariate probit dynamic panel model is then applied to estimate the effects of partnership and employment transitions in the previous year and the presence and age of children in the current year on an individual’s propensity to give or receive help. Annual data on respondents’ partnership, employment status and dependent children, and data on exchanges of help collected at 2- and 5-year intervals are used in this study. |
| Short Title | Random effects dynamic panel models for unequally spaced multivariate categorical repeated measures |
| Date Added | 11/22/2020, 8:31:16 AM |
| Modified | 11/22/2020, 8:32:30 AM |
Method is purely autoregressive without a marginal interpretation (i.e., is non-causal for treatment comparisons)
| Item Type | Journal Article |
|---|---|
| Author | Qi Liu |
| Author | Bryan E. Shepherd |
| Author | Chun Li |
| Author | Frank E. Harrell |
| Volume | 36 |
| Issue | 27 |
| Pages | 4316-4335 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | Nov 30, 2017 |
| Extra | PMID: 28872693 PMCID: PMC5675816 |
| Journal Abbr | Stat Med |
| DOI | 10.1002/sim.7433 |
| Library Catalog | PubMed |
| Language | eng |
| Abstract | We study the application of a widely used ordinal regression model, the cumulative probability model (CPM), for continuous outcomes. Such models are attractive for the analysis of continuous response variables because they are invariant to any monotonic transformation of the outcome and because they directly model the cumulative distribution function from which summaries such as expectations and quantiles can easily be derived. Such models can also readily handle mixed type distributions. We describe the motivation, estimation, inference, model assumptions, and diagnostics. We demonstrate that CPMs applied to continuous outcomes are semiparametric transformation models. Extensive simulations are performed to investigate the finite sample performance of these models. We find that properly specified CPMs generally have good finite sample performance with moderate sample sizes, but that bias may occur when the sample size is small. Cumulative probability models are fairly robust to minor or moderate link function misspecification in our simulations. For certain purposes, the CPMs are more efficient than other models. We illustrate their application, with model diagnostics, in a study of the treatment of HIV. CD4 cell count and viral load 6 months after the initiation of antiretroviral therapy are modeled using CPMs; both variables typically require transformations, and viral load has a large proportion of measurements below a detection limit. |
| Date Added | 10/15/2020, 9:46:59 PM |
| Modified | 10/15/2020, 10:02:44 PM |
| Item Type | Journal Article |
|---|---|
| Author | Val Gebski |
| Author | Karen Byth |
| Author | Rebecca Asher |
| Author | Ian Marschner |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2057 |
| Rights | © 2020 John Wiley & Sons Ltd |
| Volume | n/a |
| Issue | n/a |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2020 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2057 |
| DOI | 10.1002/pst.2057 |
| Accessed | 10/10/2020, 10:52:39 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | A model to accommodate time-to-event ordinal outcomes was proposed by Berridge and Whitehead. Very few studies have adopted this approach, despite its appeal in incorporating several ordered categories of event outcome. More recently, there has been increased interest in utilizing recurrent events to analyze practical endpoints in the study of disease history and to help quantify the changing pattern of disease over time. For example, in studies of heart failure, the analysis of a single fatal event no longer provides sufficient clinical information to manage the disease. Similarly, the grade/frequency/severity of adverse events may be more important than simply prolonged survival in studies of toxic therapies in oncology. We propose an extension of the ordinal time-to-event model to allow for multiple/recurrent events in the case of marginal models (where all subjects are at risk for each recurrence, irrespective of whether they have experienced previous recurrences) and conditional models (subjects are at risk of a recurrence only if they have experienced a previous recurrence). These models rely on marginal and conditional estimates of the instantaneous baseline hazard and provide estimates of the probabilities of an event of each severity for each recurrence over time. We outline how confidence intervals for these probabilities can be constructed and illustrate how to fit these models and provide examples of the methods, together with an interpretation of the results. |
| Date Added | 10/10/2020, 10:52:39 AM |
| Modified | 10/10/2020, 10:53:46 AM |
| Item Type | Journal Article |
|---|---|
| Author | Qi Liu |
| Author | Bryan E. Shepherd |
| Author | Chun Li |
| Author | Frank E. Harrell |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7433 |
| Rights | Copyright © 2017 John Wiley & Sons, Ltd. |
| Volume | 36 |
| Issue | 27 |
| Pages | 4316-4335 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2017 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.7433 |
| DOI | 10.1002/sim.7433 |
| Accessed | 7/15/2020, 3:01:26 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | We study the application of a widely used ordinal regression model, the cumulative probability model (CPM), for continuous outcomes. Such models are attractive for the analysis of continuous response variables because they are invariant to any monotonic transformation of the outcome and because they directly model the cumulative distribution function from which summaries such as expectations and quantiles can easily be derived. Such models can also readily handle mixed type distributions. We describe the motivation, estimation, inference, model assumptions, and diagnostics. We demonstrate that CPMs applied to continuous outcomes are semiparametric transformation models. Extensive simulations are performed to investigate the finite sample performance of these models. We find that properly specified CPMs generally have good finite sample performance with moderate sample sizes, but that bias may occur when the sample size is small. Cumulative probability models are fairly robust to minor or moderate link function misspecification in our simulations. For certain purposes, the CPMs are more efficient than other models. We illustrate their application, with model diagnostics, in a study of the treatment of HIV. CD4 cell count and viral load 6 months after the initiation of antiretroviral therapy are modeled using CPMs; both variables typically require transformations, and viral load has a large proportion of measurements below a detection limit. |
| Date Added | 7/15/2020, 3:01:26 PM |
| Modified | 7/15/2020, 3:02:08 PM |
| Item Type | Journal Article |
|---|---|
| Author | Yeongjin Gwon |
| Author | May Mo |
| Author | Ming-Hui Chen |
| Author | Zhiyi Chi |
| Author | Juan Li |
| Author | Amy H. Xia |
| Author | Joseph G. Ibrahim |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8518 |
| Rights | © 2020 John Wiley & Sons, Ltd. |
| Volume | n/a |
| Issue | n/a |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2020 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.8518 |
| DOI | 10.1002/sim.8518 |
| Accessed | 3/13/2020, 9:16:01 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Crohn's disease (CD) is a life-long condition associated with recurrent relapses characterized by abdominal pain, weight loss, anemia, and persistent diarrhea. In the US, there are approximately 780 000 CD patients and 33 000 new cases added each year. In this article, we propose a new network meta-regression approach for modeling ordinal outcomes in order to assess the efficacy of treatments for CD. Specifically, we develop regression models based on aggregate covariates for the underlying cut points of the ordinal outcomes as well as for the variances of the random effects to capture heterogeneity across trials. Our proposed models are particularly useful for indirect comparisons of multiple treatments that have not been compared head-to-head within the network meta-analysis framework. Moreover, we introduce Pearson residuals and construct an invariant test statistic to evaluate goodness-of-fit in the setting of ordinal outcome data. A detailed case study demonstrating the usefulness of the proposed methodology is carried out using aggregate ordinal outcome data from 16 clinical trials for treating CD. |
| Short Title | Network meta-regression for ordinal outcomes |
| Date Added | 3/13/2020, 9:16:01 AM |
| Modified | 3/13/2020, 9:17:12 AM |
| Item Type | Journal Article |
|---|---|
| Author | Gennaro D’Amico |
| Author | Juan G. Abraldes |
| Author | Paola Rebora |
| Author | Maria Grazia Valsecchi |
| Author | Guadalupe Garcia‐Tsao |
| URL | https://aasldpubs.onlinelibrary.wiley.com/doi/abs/10.1002/hep.31070 |
| Rights | © 2019 by the American Association for the Study of Liver Diseases. |
| Volume | n/a |
| Issue | n/a |
| Publication | Hepatology |
| ISSN | 1527-3350 |
| Date | 2020 |
| DOI | 10.1002/hep.31070 |
| Accessed | 12/19/2019, 4:54:06 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Prevention of decompensation is a primary therapeutic target in patients with compensated cirrhosis. However, a major problem is the large sample size and long follow-up required to demonstrate a significant treatment effect because of the relatively low baseline risk. For this reason, it has been recently suggested that ordinal outcomes may be used in this area to gain power and to reduce sample size. The aim of this study was to assess the applicability of ordinal outcomes in cirrhosis. An inception cohort of 202 patients with compensated cirrhosis (no ascites, gastrointestinal bleeding, encephalopathy, or jaundice) without esophageal varices was included, and 5-year outcome is reported. Etiology was mostly viral and alcoholic, and there were no dropouts. The ordinal outcome was set according to six grades with a previously established prognostic ordinality: grade 1=no disease progression; grade 2=development of varices; grade 3 = bleeding alone; grade 4=nonbleeding single decompensation; grade 5=more than one decompensating event; and grade 6=death. At the 60-month time point, patients were distributed in grades 1 through 6 as follows: 129, 43, 2, 7, 5, and 16, respectively. Emulation of a clinical trial performed by dividing patients based on baseline platelet count into two groups (cutoff 150×109/L) demonstrated a statistically significant outcome difference between groups when using ordinal outcomes not detectable by binary logistic or chi-square or time-to-event analyses. Additionally, using ordinal outcomes in a hypothetical study to prevent decompensation resulted in sample size estimates 3-to 4-fold lower than using a binary composite endpoint. Conclusion: Compared to traditional binary outcomes, the use of ordinal outcomes in trials of cirrhosis decompensation may provide more power and thus may require a smaller sample size. |
| Date Added | 12/19/2019, 4:54:06 PM |
| Modified | 12/19/2019, 4:54:46 PM |
| Item Type | Journal Article |
|---|---|
| Author | Lei Huang |
| Author | Weiqiang Hang |
| Author | Yue Chao |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8400 |
| Rights | © 2019 John Wiley & Sons, Ltd. |
| Volume | n/a |
| Issue | n/a |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2020 |
| DOI | 10.1002/sim.8400 |
| Accessed | 11/29/2019, 2:44:06 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Models for the ordered multiple categorical (OMC) response variable have already been extensively established and widely applied, but few studies have investigated linear regression problems with OMC predictors, especially in high-dimensional situations. In such settings, the pseudocategories of the discrete variable and other irrelevant explanatory variables need to be automatically selected. This paper introduces a transformation method of dummy variables for such OMC predictors, an L1 penalty regression method is proposed based on the transformation. Model selection consistency of the proposed method is derived under some common assumptions for high-dimensional situation. Both simulation studies and real data analysis present good performance of this method, showing its wide applicability in relevant regression analysis. |
| Date Added | 11/29/2019, 2:44:06 PM |
| Modified | 11/29/2019, 2:45:54 PM |
| Item Type | Journal Article |
|---|---|
| Author | N. G. Best |
| Author | D. J. Spiegelhalter |
| Author | A. Thomas |
| Author | C. E. G. Brayne |
| Volume | 159 |
| Pages | 323-342 |
| Publication | J Roy Stat Soc A |
| Date | 1996 |
| Extra | Citation Key: bes96bay tex.citeulike-article-id= 13263761 tex.posted-at= 2014-07-14 14:09:22 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | R. Brant |
| Volume | 46 |
| Pages | 1171-1178 |
| Publication | Biometrics |
| Date | 1990 |
| Extra | Citation Key: bra90 tex.citeulike-article-id= 13263792 tex.posted-at= 2014-07-14 14:09:23 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Scott R. Brazer |
| Author | Frank S. Pancotto |
| Author | Thomas T. Long III |
| Author | Frank E. Harrell |
| Author | Kerry L. Lee |
| Author | Malcolm P. Tyor |
| Author | David B. Pryor |
| Volume | 44 |
| Pages | 1263-1270 |
| Publication | J Clin Epi |
| Date | 1991 |
| Extra | Citation Key: bra91usi tex.citeulike-article-id= 13263795 tex.posted-at= 2014-07-14 14:09:23 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Bernard Rosner |
| Author | Robert J. Glynn |
| Volume | 16 |
| Pages | 357-372 |
| Publication | Stat Med |
| Date | 1997 |
| Extra | Citation Key: ros97mul tex.citeulike-article-id= 13264764 tex.posted-at= 2014-07-14 14:09:41 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | S. H. Walker |
| Author | D. B. Duncan |
| Volume | 54 |
| Pages | 167-178 |
| Publication | Biometrika |
| Date | 1967 |
| Extra | Citation Key: wal67 tex.citeulike-article-id= 13265015 tex.posted-at= 2014-07-14 14:09:47 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | A. R. Walter |
| Author | Alvan R. Feinstein |
| Author | C. K. Wells |
| Volume | 125 |
| Pages | 319-323 |
| Publication | Am J Epi |
| Date | 1987 |
| Extra | Citation Key: wal87cod tex.citeulike-article-id= 13265016 tex.posted-at= 2014-07-14 14:09:47 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | John Whitehead |
| Volume | 12 |
| Pages | 2257-2271 |
| Publication | Stat Med |
| Date | 1993 |
| Extra | Citation Key: whi93sam tex.citeulike-article-id= 13265046 tex.posted-at= 2014-07-14 14:09:48 tex.priority= 0 See letter to editor SM 15:1065-6 for binary case;see errata in SM 13:871 1994;see kol95com, jul96sam |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | T. W. Yee |
| Author | C. J. Wild |
| Volume | 58 |
| Pages | 481-493 |
| Publication | J Roy Stat Soc B |
| Date | 1996 |
| Extra | Citation Key: yee96vec tex.citeulike-article-id= 13265070 tex.posted-at= 2014-07-14 14:09:48 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Donald Hedeker |
| Author | Robert D. Gibbons |
| Volume | 50 |
| Issue | 4 |
| Pages | 933-944 |
| Publication | Biometrics |
| Date | 1994 |
| Extra | Citation Key: hed94ran tex.citeulike-article-id= 13265793 tex.posted-at= 2014-07-14 14:10:04 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Zhen Jiang |
| Author | Yimeng Liu |
| Author | Abdus S. Wahed |
| Author | Geert Molenberghs |
| URL | http://dx.doi.org/10.1002/sim.7560 |
| Volume | 37 |
| Issue | 6 |
| Pages | 983-995 |
| Publication | Stat Med |
| Date | 2018-03 |
| Extra | Citation Key: jia17joi tex.citeulike-article-id= 14502199 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.7560 tex.day= 15 tex.posted-at= 2017-12-13 13:12:21 tex.priority= 2 |
| DOI | 10.1002/sim.7560 |
| Abstract | Adherence to medication is critical in achieving effectiveness of many treatments. Factors that influence adherence behavior have been the subject of many clinical studies. Analyzing adherence is complicated because it is often measured on multiple drugs over a period, resulting in a multivariate longitudinal outcome. This paper is motivated by the Viral Resistance to Antiviral Therapy of Chronic Hepatitis C study, where adherence is measured on two drugs as a bivariate ordinal longitudinal outcome. To analyze such outcome, we propose a joint model assuming the multivariate ordinal outcome arose from a partitioned latent multivariate normal process. We also provide a flexible multilevel association structure covering both between and within outcome correlation. In simulation studies, we show that the joint model provides unbiased estimators for regression parameters, which are more efficient than those obtained through fitting separate model for each outcome. The joint method also yields unbiased estimators for the correlation parameters when the correlation structure is correctly specified. Finally, we analyze the Viral Resistance to Antiviral Therapy of Chronic Hepatitis C adherence data and discuss the findings. |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Kyeongmi Cheon |
| Author | Marie E. Thoma |
| Author | Xiangrong Kong |
| Author | Paul S Albert |
| URL | http://dx.doi.org/10.1002/sim.6151 |
| Volume | 33 |
| Issue | 18 |
| Pages | 3204-3213 |
| Publication | Stat Med |
| Date | 2014-08 |
| Extra | Citation Key: che14mx tex.citeulike-article-id= 13448117 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.6151 tex.day= 15 tex.posted-at= 2014-11-29 16:17:13 tex.priority= 2 |
| DOI | 10.1002/sim.6151 |
| Abstract | Markov models used to analyze transition patterns in discrete longitudinal data are based on the limiting assumption that individuals follow the common underlying transition process. However, when one is interested in diseases with different disease or severity subtypes, explicitly modeling subpopulation-specific transition patterns may be appropriate. We propose a model which captures heterogeneity in the transition process through a finite mixture model formulation and provides a framework for identifying subpopulations at different risks. We apply the procedure to longitudinal bacterial vaginosis study data and demonstrate that the model fits the data well. Further, we show that under the mixture model formulation, we can make the important distinction between how covariates affect transition patterns unique to each of the subpopulations and how they affect which subgroup a participant will belong to. Practically, covariate effects on subpopulation-specific transition behavior and those on subpopulation membership can be interpreted as effects on short-term and long-term transition behavior. We further investigate models with higher-order subpopulation-specific transition dependence. |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Gaohong Dong |
| Author | Di Li |
| Author | Steffen Ballerstedt |
| Author | Marc Vandemeulebroecke |
| URL | http://dx.doi.org/10.1002/pst.1763 |
| Publication | Pharm Stat |
| ISSN | 15391604 |
| Date | 2016 |
| Extra | Citation Key: don16gen tex.citeulike-article-id= 14112183 tex.citeulike-attachment-1= don16gen.pdf; /pdf/user/harrelfe/article/14112183/1080330/don16gen.pdf; 6569d6cd68301833bd0ac954ea138d7477c4164e tex.citeulike-linkout-0= http://dx.doi.org/10.1002/pst.1763 tex.posted-at= 2016-08-12 12:48:49 tex.priority= 3 |
| DOI | 10.1002/pst.1763 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Chun Li |
| Author | Bryan E. Shepherd |
| URL | http://biomet.oxfordjournals.org/content/99/2/473.abstract |
| Volume | 99 |
| Issue | 2 |
| Pages | 473-480 |
| Publication | Biometrika |
| Date | 2012 |
| Extra | Citation Key: li12new tex.citeulike-article-id= 13265929 tex.citeulike-linkout-0= http://dx.doi.org/10.1093/biomet/asr073 tex.citeulike-linkout-1= http://biomet.oxfordjournals.org/content/99/2/473.abstract tex.eprint= http://biomet.oxfordjournals.org/content/99/2/473.full.pdf+html tex.posted-at= 2014-07-14 14:10:07 tex.priority= 0 |
| DOI | 10.1093/biomet/asr073 |
| Abstract | We propose a new residual for regression models of ordinal outcomes, defined as Esign(y,Y), where y is the observed outcome and Y is a random variable from the fitted distribution. This new residual is a single value per subject irrespective of the number of categories of the ordinal outcome, contains directional information between the observed value and the fitted distribution, and does not require the assignment of arbitrary numbers to categories. We study its properties, describe its connections with other residuals, ranks and ridits, and demonstrate its use in model diagnostics. |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Morten W. Fagerland |
| Author | David W. Hosmer |
| URL | http://dx.doi.org/10.1002/sim.5645 |
| Volume | 32 |
| Issue | 13 |
| Pages | 2235-2249 |
| Publication | Stat Med |
| Date | 2013 |
| Extra | Citation Key: fag13goo tex.citeulike-article-id= 13265966 tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.5645 tex.posted-at= 2014-07-14 14:10:08 tex.priority= 0 |
| DOI | 10.1002/sim.5645 |
| Abstract | We examine goodness-of-fit tests for the proportional odds logistic regression model—the most commonly used regression model for an ordinal response variable. We derive a test statistic based on the Hosmer–Lemeshow test for binary logistic regression. Using a simulation study, we investigate the distribution and power properties of this test and compare these with those of three other goodness-of-fit tests. The new test has lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. Moreover, the test allows for the results to be summarized in a contingency table of observed and estimated frequencies, which is a useful supplementary tool to assess model fit. We illustrate the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents. The test proposed in this paper is similar to a recently developed goodness-of-fit test for multinomial logistic regression. A unified approach for testing goodness of fit is now available for binary, multinomial, and ordinal logistic regression models. |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | N. R. Parsons |
| Author | R. N. Edmondson |
| Author | S. G. Gilmour |
| URL | http://www.jstor.org/stable/3879106 |
| Volume | 55 |
| Issue | 4 |
| Publication | Appl Stat |
| Date | 2006 |
| Extra | Citation Key: par06gen tex.citeulike-article-id= 13265993 tex.citeulike-linkout-0= http://www.jstor.org/stable/3879106 tex.posted-at= 2014-07-14 14:10:09 tex.priority= 0 |
| Abstract | Generalized estimating equations for correlated repeated ordinal score data are developed assuming a proportional odds model and a working correlation structure based on a first-order autoregressive process. Repeated ordinal scores on the same experimental units, not necessarily with equally spaced time intervals, are assumed and a new algorithm for the joint estimation of the model regression parameters and the correlation coefficient is developed. Approximate standard errors for the estimated correlation coefficient are developed and a simulation study is used to compare the new methodology with existing methodology. The work was part of a project on post-harvest quality of pot-plants and the generalized estimating equation model is used to analyse data on poinsettia and begonia pot-plant quality deterioration over time. The relationship between the key attributes of plant quality and the quality and longevity of ornamental pot-plants during shelf and after-sales life is explored. |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Robert H. Lyles |
| Author | Hung-Mo Lin |
| Author | John M. Williamson |
| Volume | 26 |
| Pages | 1632-1648 |
| Publication | Stat Med |
| Date | 2007 |
| Extra | Citation Key: lyl07pra tex.citeulike-article-id= 13265564 tex.posted-at= 2014-07-14 14:09:59 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Jeremy M. G. Taylor |
| Author | Lu Wang |
| Author | Zhiguo Li |
| Volume | 26 |
| Pages | 3443-3458 |
| Publication | Stat Med |
| Date | 2007 |
| Extra | Citation Key: tay07ana tex.citeulike-article-id= 13265611 tex.posted-at= 2014-07-14 14:10:00 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Antoine Guisan |
| Author | Frank E. Harrell |
| Volume | 11 |
| Pages | 617-626 |
| Publication | J Veg Sci |
| Date | 2000 |
| Extra | Citation Key: gui00ord tex.citeulike-article-id= 13265110 tex.posted-at= 2014-07-14 14:09:49 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | David J. Lunn |
| Author | Jon Wakefield |
| Author | Amy Racine-Poon |
| Volume | 20 |
| Pages | 2261-2285 |
| Publication | Stat Med |
| Date | 2001 |
| Extra | Citation Key: lun01cum tex.citeulike-article-id= 13265215 tex.posted-at= 2014-07-14 14:09:52 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Ralf Bender |
| Author | Axel Benner |
| Volume | 42 |
| Pages | 677-699 |
| Publication | Biometrical J |
| Date | 2000 |
| Extra | Citation Key: ben00cal tex.citeulike-article-id= 13265258 tex.posted-at= 2014-07-14 14:09:53 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
extensive use of Design library
| Item Type | Journal Article |
|---|---|
| Author | Peter A. Lachenbruch |
| Volume | 22 |
| Pages | 3823-3842 |
| Publication | Stat Med |
| Date | 2003 |
| Extra | Citation Key: lac03pro tex.citeulike-article-id= 13265360 tex.posted-at= 2014-07-14 14:09:55 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | S. M. Snapinn |
| Author | R. D. Small |
| Volume | 42 |
| Pages | 583-592 |
| Publication | Biometrics |
| Date | 1986 |
| Extra | Citation Key: sna86 tex.citeulike-article-id= 13264880 tex.posted-at= 2014-07-14 14:09:44 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Ming Tan |
| Author | Yinsheng Qu |
| Author | Ed Mascha |
| Author | Armin Schubert |
| Volume | 18 |
| Pages | 1983-1992 |
| Publication | Stat Med |
| Date | 1999 |
| Extra | Citation Key: tan99bay tex.citeulike-article-id= 13264930 tex.posted-at= 2014-07-14 14:09:46 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | D. O. Stram |
| Author | L. J. Wei |
| Author | Ware |
| Volume | 83 |
| Pages | 631-637 |
| Publication | J Am Stat Assoc |
| Date | 1988 |
| Extra | Citation Key: str88 tex.citeulike-article-id= 13264914 tex.posted-at= 2014-07-14 14:09:45 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Mark D. Thornquist |
| Volume | 49 |
| Pages | 721-730 |
| Publication | Biometrics |
| Date | 1993 |
| Extra | Citation Key: tho93pro tex.citeulike-article-id= 13264952 tex.posted-at= 2014-07-14 14:09:46 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Thomas R. Ten Have |
| Volume | 52 |
| Pages | 473-491 |
| Publication | Biometrics |
| Date | 1996 |
| Extra | Citation Key: ten96mix tex.citeulike-article-id= 13264938 tex.posted-at= 2014-07-14 14:09:46 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Peter McCullagh |
| Volume | 42 |
| Pages | 109-142 |
| Publication | J Roy Stat Soc B |
| Date | 1980 |
| Extra | Citation Key: mcc80reg tex.citeulike-article-id= 13264586 tex.posted-at= 2014-07-14 14:09:38 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | G. Molenberghs |
| Author | M. G. Kenward |
| Author | E. Lesaffre |
| Volume | 84 |
| Pages | 33-44 |
| Publication | Biometrika |
| Date | 1997 |
| Extra | Citation Key: mol97ana tex.citeulike-article-id= 13264602 tex.posted-at= 2014-07-14 14:09:38 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Margaret S. Pepe |
| Volume | 84 |
| Pages | 595-608 |
| Publication | Biometrika |
| Date | 1997 |
| Extra | Citation Key: pep97reg tex.citeulike-article-id= 13264670 tex.posted-at= 2014-07-14 14:09:39 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Emmanuel Lesaffre |
| Author | Geert Molenberghs |
| Author | Lode Dewulf |
| Volume | 15 |
| Pages | 1123-1141 |
| Publication | Stat Med |
| Date | 1996 |
| Extra | Citation Key: les96eff tex.citeulike-article-id= 13264502 tex.posted-at= 2014-07-14 14:09:36 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Frank E. Harrell |
| Author | Peter A. Margolis |
| Author | Sandy Gove |
| Author | Karen E. Mason |
| Author | E. Kim Mulholland |
| Author | Deborah Lehmann |
| Author | Lulu Muhe |
| Author | Salvacion Gatchalian |
| Author | Heinz F. Eichenwald |
| URL | http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1097-0258(19980430)17:8%3C909::AID-SIM753%3E3.0.CO;2-O/abstract |
| Volume | 17 |
| Pages | 909-944 |
| Publication | Stat Med |
| Date | 1998 |
| Extra | Citation Key: har98dev tex.citeulike-article-id= 13264261 tex.citeulike-linkout-0= http://dx.doi.org/ tex.citeulike-linkout-1= http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1097-0258(19980430)17:8%3C909::AID-SIM753%3E3.0.CO;2-O/abstract tex.posted-at= 2014-07-14 14:09:32 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | T. J. Hastie |
| Author | J. L. Botha |
| Author | C. M. Schnitzler |
| Volume | 8 |
| Pages | 785-794 |
| Publication | Stat Med |
| Date | 1989 |
| Extra | Citation Key: has89 tex.citeulike-article-id= 13264267 tex.posted-at= 2014-07-14 14:09:32 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Timothy Heeren |
| Author | Ralph D'Agostino |
| Volume | 6 |
| Pages | 79-90 |
| Publication | Stat Med |
| Date | 1987 |
| Extra | Citation Key: hee87rob tex.citeulike-article-id= 13264281 tex.posted-at= 2014-07-14 14:09:32 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Book Section |
|---|---|
| Author | Gary G. Koch |
| Author | Ingrid A. Amara |
| Author | Julio M. Singer |
| Editor | P. K. Sen |
| Place | Amsterdam |
| Publisher | Elsevier Science Publishers B. V. (North-Holland) |
| Date | 1985 |
| Extra | Citation Key: koc85two tex.citeulike-article-id= 13264415 tex.posted-at= 2014-07-14 14:09:35 tex.priority= 0 |
| Book Title | BIOSTATISTICS: Statistics in Biomedical, Public Health and Environmental Sciences |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | E. Läärä |
| Author | J. N. S. Matthews |
| Volume | 72 |
| Pages | 206-7 |
| Publication | Biometrika |
| Date | 1985 |
| Extra | Citation Key: laa85equ tex.citeulike-article-id= 13264441 tex.posted-at= 2014-07-14 14:09:35 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | D. Follmann |
| Author | J. Wittes |
| Author | J. A. Cutler |
| Volume | 11 |
| Pages | 427-437 |
| Publication | Stat Med |
| Date | 1992 |
| Extra | Citation Key: fol92use tex.citeulike-article-id= 13264088 tex.posted-at= 2014-07-14 14:09:28 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Stephen J. Gange |
| Author | Kathryn L. P. Linton |
| Author | Alastair J. Scott |
| Author | David L. DeMets |
| Author | Ronald Klein |
| Volume | 14 |
| Pages | 1961-1974 |
| Publication | Stat Med |
| Date | 1995 |
| Extra | Citation Key: gan95com tex.citeulike-article-id= 13264117 tex.posted-at= 2014-07-14 14:09:29 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Doksum K. A. Dabrowska DM |
| Volume | 83 |
| Pages | 744-749 |
| Publication | J Am Stat Assoc |
| Date | 1988 |
| Extra | Citation Key: dab88 tex.citeulike-article-id= 13263960 tex.posted-at= 2014-07-14 14:09:26 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Sander Greenland |
| Volume | 13 |
| Pages | 1665-1677 |
| Publication | Stat Med |
| Date | 1994 |
| Extra | Citation Key: gre94alt tex.citeulike-article-id= 13264180 tex.posted-at= 2014-07-14 14:09:30 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Murray Hannah |
| Author | Paul Quigley |
| Volume | 52 |
| Pages | 771-775 |
| Publication | Biometrics |
| Date | 1996 |
| Extra | Citation Key: han96pre tex.citeulike-article-id= 13264212 tex.posted-at= 2014-07-14 14:09:31 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | G. J. Carr |
| Author | K. B. Hafner |
| Author | G. G. Koch |
| Volume | 84 |
| Pages | 797-804 |
| Publication | J Am Stat Assoc |
| Date | 1989 |
| Extra | Citation Key: car89 tex.citeulike-article-id= 13263859 tex.posted-at= 2014-07-14 14:09:24 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Campbell |
| Author | A. Donner |
| Volume | 84 |
| Pages | 587-591 |
| Publication | J Am Stat Assoc |
| Date | 1989 |
| Extra | Citation Key: cam89 tex.citeulike-article-id= 13263857 tex.posted-at= 2014-07-14 14:09:24 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | T. J. Cole |
| Author | C. J. Morley |
| Author | A. J. Thornton |
| Author | M. A. Fowler |
| Author | P. H. Hewson |
| Volume | 154 |
| Pages | 287-304 |
| Publication | J Roy Stat Soc A |
| Date | 1991 |
| Extra | Citation Key: col91sco tex.citeulike-article-id= 13263907 tex.posted-at= 2014-07-14 14:09:25 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Christopher Cox |
| Volume | 14 |
| Pages | 1191-1203 |
| Publication | Stat Med |
| Date | 1995 |
| Extra | Citation Key: cox95loc tex.citeulike-article-id= 13263944 tex.posted-at= 2014-07-14 14:09:26 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | John J. Dziak |
| Author | Runze Li |
| Author | Marc A. Zimmerman |
| Author | Anne Buu |
| URL | http://dx.doi.org/10.1002/sim.6303 |
| Volume | 33 |
| Issue | 29 |
| Pages | 5126-5137 |
| Publication | Stat Med |
| Date | 2014-12 |
| Extra | Citation Key: dzi14tim tex.citeulike-article-id= 13444238 tex.citeulike-attachment-1= dzi14tim.pdf; /pdf/user/harrelfe/article/13444238/995482/dzi14tim.pdf; 02de672109de22707a31d0271d1e427671d959bf tex.citeulike-linkout-0= http://dx.doi.org/10.1002/sim.6303 tex.day= 20 tex.posted-at= 2014-11-24 18:19:44 tex.priority= 0 |
| DOI | 10.1002/sim.6303 |
| Abstract | Ordinal responses are very common in longitudinal data collected from substance abuse research or other behavioral research. This study develops a new statistical model with free SAS macros that can be applied to characterize time-varying effects on ordinal responses. Our simulation study shows that the ordinal-scale time-varying effects model has very low estimation bias and sometimes offers considerably better performance when fitting data with ordinal responses than a model that treats the response as continuous. Contrary to a common assumption that an ordinal scale with several levels can be treated as continuous, our results indicate that it is not so much the number of levels on the ordinal scale but rather the skewness of the distribution that makes a difference on relative performance of linear versus ordinal models. We use longitudinal data from a well-known study on youth at high risk for substance abuse as a motivating example to demonstrate that the proposed model can characterize the time-varying effect of negative peer influences on alcohol use in a way that is more consistent with the developmental theory and existing literature, in comparison with the linear time-varying effect model. |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | A. Agresti |
| Volume | 8 |
| Pages | 1209-1224 |
| Publication | Stat Med |
| Date | 1989 |
| Extra | Citation Key: agr89 tex.citeulike-article-id= 13263674 tex.posted-at= 2014-07-14 14:09:21 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | J. A. Anderson |
| Author | P. R. Philips |
| Volume | 30 |
| Pages | 22-31 |
| Publication | Appl Stat |
| Date | 1981 |
| Extra | Citation Key: and81reg tex.citeulike-article-id= 13263697 tex.posted-at= 2014-07-14 14:09:21 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | J. A. Anderson |
| Volume | 46 |
| Pages | 1-30 |
| Publication | J Roy Stat Soc B |
| Date | 1984 |
| Extra | Citation Key: and84reg tex.citeulike-article-id= 13263700 tex.posted-at= 2014-07-14 14:09:21 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | D. Ashby |
| Author | C. R. West |
| Author | D. Ames |
| Volume | 8 |
| Pages | 1317-1326 |
| Publication | Stat Med |
| Date | 1989 |
| Extra | Citation Key: ash89 tex.citeulike-article-id= 13263711 tex.posted-at= 2014-07-14 14:09:21 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Peter Bajorski |
| Author | John Petkau |
| Volume | 94 |
| Pages | 970-978 |
| Publication | J Am Stat Assoc |
| Date | 1999 |
| Extra | Citation Key: baj99non tex.citeulike-article-id= 13263719 tex.posted-at= 2014-07-14 14:09:21 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
analysis of change in ordinal response variable
| Item Type | Journal Article |
|---|---|
| Author | B. G. Armstrong |
| Author | M. Sloan |
| Volume | 129 |
| Pages | 191-204 |
| Publication | Am J Epi |
| Date | 1989 |
| Extra | Citation Key: arm89 tex.citeulike-article-id= 13263709 tex.posted-at= 2014-07-14 14:09:21 tex.priority= 0 See letter to editor by Peterson |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
| Item Type | Journal Article |
|---|---|
| Author | Maurizio Manuguerra |
| Author | Gillian Z. Heller |
| URL | http://dx.doi.org/10.2202/1557-4679.1230 |
| Volume | 6 |
| Issue | 1 |
| Publication | Int J Biostat |
| ISSN | 1557-4679 |
| Date | 2010-01 |
| Extra | Citation Key: man10ord tex.citeulike-article-id= 14232080 tex.citeulike-attachment-1= man10ord.pdf; /pdf/user/harrelfe/article/14232080/1095623/man10ord.pdf; f785bd1ab161455888b01617df621ddd57f8daa0 tex.citeulike-linkout-0= http://dx.doi.org/10.2202/1557-4679.1230 tex.day= 6 tex.posted-at= 2016-12-22 15:03:30 tex.priority= 0 |
| DOI | 10.2202/1557-4679.1230 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
mislabeled a flexible parametric model as semi-parametric; does not cover semi-parametric approach with lots of intercepts
| Item Type | Journal Article |
|---|---|
| Author | Elizabeth Colantuoni |
| Author | Daniel O. Scharfstein |
| Author | Chenguang Wang |
| Author | Mohamed D. Hashem |
| Author | Andrew Leroux |
| Author | Dale M. Needham |
| Author | Timothy D. Girard |
| URL | https://www.bmj.com/content/360/bmj.j5748 |
| Rights | Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
| Volume | 360 |
| Pages | j5748 |
| Publication | BMJ |
| ISSN | 0959-8138, 1756-1833 |
| Date | 2018/01/03 |
| Extra | PMID: 29298779 |
| Journal Abbr | BMJ |
| DOI | 10.1136/bmj.j5748 |
| Accessed | 9/7/2019, 1:15:40 PM |
| Library Catalog | www.bmj.com |
| Language | en |
| Abstract | <p>Mortality is a common primary endpoint in randomized controlled trials of patients with a high severity of illness, such as critically ill patients. However, researchers are increasingly evaluating functional outcomes, such as quality of life. Importantly, in such trials some patients may die before the assessment of a functional outcome, resulting in the functional outcome being “truncated due to death.” As described in this paper, defining and testing treatment effects on functional outcomes in this setting requires careful consideration. Data from a completed trial of critically ill patients are used to highlight key differences among three statistical approaches used when analyzing such trials.</p> |
| Date Added | 9/7/2019, 1:15:40 PM |
| Modified | 9/7/2019, 1:16:18 PM |
| Item Type | Journal Article |
|---|---|
| Author | Scott R. Evans |
| Author | Dean Follmann |
| URL | https://doi.org/10.1080/19466315.2016.1207561 |
| Volume | 8 |
| Issue | 4 |
| Pages | 386-393 |
| Publication | Statistics in Biopharmaceutical Research |
| ISSN | null |
| Date | October 1, 2016 |
| DOI | 10.1080/19466315.2016.1207561 |
| Accessed | 8/6/2019, 1:53:08 PM |
| Library Catalog | Taylor and Francis+NEJM |
| Abstract | In the future, clinical trials will have an increased emphasis on pragmatism, providing a practical description of the effects of new treatments in realistic clinical settings. Accomplishing pragmatism requires better summaries of the totality of the evidence in ways that clinical trials consumers—patients, physicians, insurers—find transparent and allow for informed benefit:risk decision-making.The current approach to the analysis of clinical trials is to analyze efficacy and safety separately and then combine these analyses into a benefit:risk assessment. Many assume that this will effectively describe the impact on patients. But this approach is suboptimal for evaluating the totality of effects on patients.We discuss methods for benefit:risk assessment that have greater pragmatism than methods that separately analyze efficacy and safety. These include the concepts of within-patient analyses and composite benefit:risk endpoints with a goal of understanding how to analyze one patient before trying to figure out how to analyze many. We discuss the desirability of outcome ranking (DOOR) and introduce the partial credit strategy using an example in a clinical trial evaluating the effects of a new antibiotic. As part of the example, we introduce a strategy to engage patients as a resource to inform benefit:risk analyses consistent with the goal of measuring and weighing outcomes that are most important from the patient's perspective.We describe a broad vision for the future of clinical trials consistent with increased pragmatism. Greater focus on using endpoints to analyze patients rather than patients to analyze endpoints particularly in late-phase/stage clinical trials is an important part of this vision. |
| Short Title | Using Outcomes to Analyze Patients Rather than Patients to Analyze Outcomes |
| Date Added | 8/6/2019, 1:53:08 PM |
| Modified | 8/6/2019, 1:56:20 PM |
Proposed methods do not handle time until events fully. For example there is no discussion of how to trade off an early heart attack with a late death. Also does not allow for interim missing data.
Wrongly implied that the Wilcoxon test does not assume the proportional odds assumption (p. 10 bottom).
| Item Type | Journal Article |
|---|---|
| Author | Scott R. Evans |
| Author | Daniel Rubin |
| Author | Dean Follmann |
| Author | Gene Pennello |
| Author | W. Charles Huskins |
| Author | John H. Powers |
| Author | David Schoenfeld |
| Author | Christy Chuang-Stein |
| Author | Sara E. Cosgrove |
| Author | Vance G. Fowler |
| Author | Ebbing Lautenbach |
| Author | Henry F. Chambers |
| Volume | 61 |
| Issue | 5 |
| Pages | 800-806 |
| Publication | Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America |
| ISSN | 1537-6591 |
| Date | Sep 01, 2015 |
| Extra | PMID: 26113652 PMCID: PMC4542892 |
| Journal Abbr | Clin. Infect. Dis. |
| DOI | 10.1093/cid/civ495 |
| Library Catalog | PubMed |
| Language | eng |
| Abstract | Clinical trials that compare strategies to optimize antibiotic use are of critical importance but are limited by competing risks that distort outcome interpretation, complexities of noninferiority trials, large sample sizes, and inadequate evaluation of benefits and harms at the patient level. The Antibacterial Resistance Leadership Group strives to overcome these challenges through innovative trial design. Response adjusted for duration of antibiotic risk (RADAR) is a novel methodology utilizing a superiority design and a 2-step process: (1) categorizing patients into an overall clinical outcome (based on benefits and harms), and (2) ranking patients with respect to a desirability of outcome ranking (DOOR). DOORs are constructed by assigning higher ranks to patients with (1) better overall clinical outcomes and (2) shorter durations of antibiotic use for similar overall clinical outcomes. DOOR distributions are compared between antibiotic use strategies. The probability that a randomly selected patient will have a better DOOR if assigned to the new strategy is estimated. DOOR/RADAR represents a new paradigm in assessing the risks and benefits of new strategies to optimize antibiotic use. |
| Date Added | 8/6/2019, 1:55:30 PM |
| Modified | 8/6/2019, 1:56:11 PM |
| Item Type | Journal Article |
|---|---|
| Author | Trevelyan J. McKinley |
| Author | Michelle Morters |
| Author | James L. N. Wood |
| URL | https://projecteuclid.org/euclid.ba/1422468421 |
| Volume | 10 |
| Issue | 1 |
| Pages | 1-30 |
| Publication | Bayesian Analysis |
| ISSN | 1936-0975, 1931-6690 |
| Date | 2015-03 |
| Extra | MR: MR3420895 Zbl: 1334.62141 |
| Journal Abbr | Bayesian Anal. |
| DOI | 10.1214/14-BA884 |
| Accessed | 1/19/2019, 8:07:15 AM |
| Library Catalog | Project Euclid |
| Language | EN |
| Abstract | The use of the proportional odds (PO) model for ordinal regression is ubiquitous in the literature. If the assumption of parallel lines does not hold for the data, then an alternative is to specify a non-proportional odds (NPO) model, where the regression parameters are allowed to vary depending on the level of the response. However, it is often difficult to fit these models, and challenges regarding model choice and fitting are further compounded if there are a large number of explanatory variables. We make two contributions towards tackling these issues: firstly, we develop a Bayesian method for fitting these models, that ensures the stochastic ordering conditions hold for an arbitrary finite range of the explanatory variables, allowing NPO models to be fitted to any observed data set. Secondly, we use reversible-jump Markov chain Monte Carlo to allow the model to choose between PO and NPO structures for each explanatory variable, and show how variable selection can be incorporated. These methods can be adapted for any monotonic increasing link functions. We illustrate the utility of these approaches on novel data from a longitudinal study of individual-level risk factors affecting body condition score in a dog population in Zenzele, South Africa. |
| Date Added | 1/19/2019, 8:07:15 AM |
| Modified | 1/19/2019, 8:07:57 AM |
| Item Type | Journal Article |
|---|---|
| Author | Torrin M. Liddell |
| Author | John K. Kruschke |
| URL | http://www.sciencedirect.com/science/article/pii/S0022103117307746 |
| Volume | 79 |
| Pages | 328-348 |
| Publication | Journal of Experimental Social Psychology |
| ISSN | 0022-1031 |
| Date | November 1, 2018 |
| Journal Abbr | Journal of Experimental Social Psychology |
| DOI | 10.1016/j.jesp.2018.08.009 |
| Accessed | 1/7/2019, 1:55:52 PM |
| Library Catalog | ScienceDirect |
| Abstract | We surveyed all articles in the Journal of Personality and Social Psychology (JPSP), Psychological Science (PS), and the Journal of Experimental Psychology: General (JEP:G) that mentioned the term “Likert,” and found that 100% of the articles that analyzed ordinal data did so using a metric model. We present novel evidence that analyzing ordinal data as if they were metric can systematically lead to errors. We demonstrate false alarms (i.e., detecting an effect where none exists, Type I errors) and failures to detect effects (i.e., loss of power, Type II errors). We demonstrate systematic inversions of effects, for which treating ordinal data as metric indicates the opposite ordering of means than the true ordering of means. We show the same problems — false alarms, misses, and inversions — for interactions in factorial designs and for trend analyses in regression. We demonstrate that averaging across multiple ordinal measurements does not solve or even ameliorate these problems. A central contribution is a graphical explanation of how and when the misrepresentations occur. Moreover, we point out that there is no sure-fire way to detect these problems by treating the ordinal values as metric, and instead we advocate use of ordered-probit models (or similar) because they will better describe the data. Finally, although frequentist approaches to some ordered-probit models are available, we use Bayesian methods because of their flexibility in specifying models and their richness and accuracy in providing parameter estimates. An R script is provided for running an analysis that compares ordered-probit and metric models. |
| Short Title | Analyzing ordinal data with metric models |
| Date Added | 1/7/2019, 1:55:52 PM |
| Modified | 1/7/2019, 1:56:25 PM |
| Item Type | Journal Article |
|---|---|
| Author | Paul-Christian Bürkner |
| Author | Matti Vuorre |
| URL | https://psyarxiv.com/x8swp/ |
| Date | 2018-02-28T23:41:26.334Z |
| DOI | 10.31234/osf.io/x8swp |
| Accessed | 1/7/2019, 1:52:26 PM |
| Library Catalog | psyarxiv.com |
| Abstract | Ordinal variables, while extremely common in Psychology, are almost exclusively analysed with statistical models that falsely assume them to be metric. This practice can lead to distorted effect size estimates, inflated error rates, and other problems. We argue for the application of ordinal models that make appropriate assumptions about the variables under study. In this tutorial article, we first explain the three major ordinal model classes; the cumulative, sequential and adjacent category models. We then show how to fit ordinal models in a fully Bayesian framework with the R package brms, using data sets on stem cell opinions and marriage time courses. Appendices provide detailed mathematical derivations of the models and a discussion of censored ordinal models. Ordinal models provide better theoretical interpretation and numerical inference from ordinal data, and we recommend their widespread adoption in Psychology. |
| Short Title | Ordinal Regression Models in Psychology |
| Date Added | 1/7/2019, 1:52:26 PM |
| Modified | 1/7/2019, 1:54:01 PM |
| Item Type | Journal Article |
|---|---|
| Author | Martin Happ |
| Author | Arne C. Bathke |
| Author | Edgar Brunner |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7983 |
| Rights | © 2018 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. |
| Volume | 38 |
| Issue | 3 |
| Pages | 363-375 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2019 |
| DOI | 10.1002/sim.7983 |
| Accessed | 1/5/2019, 1:09:55 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | There are many different proposed procedures for sample size planning for the Wilcoxon-Mann-Whitney test at given type-I and type-II error rates α and β, respectively. Most methods assume very specific models or types of data to simplify calculations (eg, ordered categorical or metric data, location shift alternatives, etc). We present a unified approach that covers metric data with and without ties, count data, ordered categorical data, and even dichotomous data. For that, we calculate the unknown theoretical quantities such as the variances under the null and relevant alternative hypothesis by considering the following “synthetic data” approach. We evaluate data whose empirical distribution functions match the theoretical distribution functions involved in the computations of the unknown theoretical quantities. Then, well-known relations for the ranks of the data are used for the calculations. In addition to computing the necessary sample size N for a fixed allocation proportion t = n1/N, where n1 is the sample size in the first group and N = n1 + n2 is the total sample size, we provide an interval for the optimal allocation rate t, which minimizes the total sample size N. It turns out that, for certain distributions, a balanced design is optimal. We give a characterization of such distributions. Furthermore, we show that the optimal choice of t depends on the ratio of the two variances, which determine the variance of the Wilcoxon-Mann-Whitney statistic under the alternative. This is different from an optimal sample size allocation in case of the normal distribution model. |
| Date Added | 1/5/2019, 1:09:55 PM |
| Modified | 1/5/2019, 1:10:27 PM |
| Item Type | Journal Article |
|---|---|
| Author | Jan De Neve |
| Author | Olivier Thas |
| Author | Thomas A. Gerds |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8078 |
| Volume | 0 |
| Issue | 0 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2019 |
| DOI | 10.1002/sim.8078 |
| Accessed | 1/5/2019, 1:04:01 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Semiparametric linear transformation models form a versatile class of regression models with the Cox proportional hazards model being the most well-known member. These models are well studied for right censored outcomes and are typically used in survival analysis. We consider transformation models as a tool for situations with uncensored continuous outcomes where linear regression is not appropriate. We introduce the probabilistic index as a uniform effect measure for the class of transformation models. We discuss and compare three estimators using a working Cox regression model: the partial likelihood estimator, an estimator based on binary generalized linear models and one based on probabilistic index model estimating equations. The latter has a superior performance in terms of bias and variance when the working model is misspecified. For the purpose of illustration, we analyze data that were collected at an urban alcohol and drug detoxification unit. |
| Short Title | Semiparametric linear transformation models |
| Date Added | 1/5/2019, 1:04:01 PM |
| Modified | 1/5/2019, 1:04:44 PM |
| Item Type | Journal Article |
|---|---|
| Author | Martin Happ |
| Author | Arne C. Bathke |
| Author | Edgar Brunner |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7983 |
| Rights | © 2018 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. |
| Volume | 0 |
| Issue | 0 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2018 |
| DOI | 10.1002/sim.7983 |
| Accessed | 10/12/2018, 10:21:16 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | There are many different proposed procedures for sample size planning for the Wilcoxon-Mann-Whitney test at given type-I and type-II error rates α and β, respectively. Most methods assume very specific models or types of data to simplify calculations (eg, ordered categorical or metric data, location shift alternatives, etc). We present a unified approach that covers metric data with and without ties, count data, ordered categorical data, and even dichotomous data. For that, we calculate the unknown theoretical quantities such as the variances under the null and relevant alternative hypothesis by considering the following “synthetic data” approach. We evaluate data whose empirical distribution functions match the theoretical distribution functions involved in the computations of the unknown theoretical quantities. Then, well-known relations for the ranks of the data are used for the calculations. In addition to computing the necessary sample size N for a fixed allocation proportion t = n1/N, where n1 is the sample size in the first group and N = n1 + n2 is the total sample size, we provide an interval for the optimal allocation rate t, which minimizes the total sample size N. It turns out that, for certain distributions, a balanced design is optimal. We give a characterization of such distributions. Furthermore, we show that the optimal choice of t depends on the ratio of the two variances, which determine the variance of the Wilcoxon-Mann-Whitney statistic under the alternative. This is different from an optimal sample size allocation in case of the normal distribution model. |
| Date Added | 10/12/2018, 10:21:16 AM |
| Modified | 10/12/2018, 10:22:31 AM |