| Type | Journal Article |
|---|---|
| Author | Jody D. Ciolino |
| Author | Cathie Spino |
| Author | Walter T. Ambrosius |
| Author | Shokoufeh Khalatbari |
| Author | Shari Messinger Cayetano |
| Author | Jodi A. Lapidus |
| Author | Paul J. Nietert |
| Author | Robert A. Oster |
| Author | Susan M. Perkins |
| Author | Brad H. Pollock |
| Author | Gina-Maria Pomann |
| Author | Lori Lyn Price |
| Author | Todd W. Rice |
| Author | Tor D. Tosteson |
| Author | Christopher J. Lindsell |
| Author | Heidi Spratt |
| URL | https://www.cambridge.org/core/journals/journal-of-clinical-and-translational-science/article/guidance-for-biostatisticians-on-their-essential-contributions-to-clinical-and-translational-research-protocol-review/49B4D365DC2676532BDBDD922298D273# |
| Volume | 5 |
| Issue | 1 |
| Publication | Journal of Clinical and Translational Science |
| ISSN | 2059-8661 |
| Date | 2021/ed |
| Extra | Publisher: Cambridge University Press |
| DOI | 10.1017/cts.2021.814 |
| Accessed | 10/28/2021, 8:19:22 AM |
| Library Catalog | Cambridge University Press |
| Language | en |
| Abstract | Rigorous scientific review of research protocols is critical to making funding decisions, and to the protection of both human and non-human research participants. Given the increasing complexity of research designs and data analysis methods, quantitative experts, such as biostatisticians, play an essential role in evaluating the rigor and reproducibility of proposed methods. However, there is a common misconception that a statistician’s input is relevant only to sample size/power and statistical analysis sections of a protocol. The comprehensive nature of a biostatistical review coupled with limited guidance on key components of protocol review motived this work. Members of the Biostatistics, Epidemiology, and Research Design Special Interest Group of the Association for Clinical and Translational Science used a consensus approach to identify the elements of research protocols that a biostatistician should consider in a review, and provide specific guidance on how each element should be reviewed. We present the resulting review framework as an educational tool and guideline for biostatisticians navigating review boards and panels. We briefly describe the approach to developing the framework, and we provide a comprehensive checklist and guidance on review of each protocol element. We posit that the biostatistical reviewer, through their breadth of engagement across multiple disciplines and experience with a range of research designs, can and should contribute significantly beyond review of the statistical analysis plan and sample size justification. Through careful scientific review, we hope to prevent excess resource expenditure and risk to humans and animals on poorly planned studies. |
| Date Added | 10/28/2021, 8:19:22 AM |
| Modified | 10/28/2021, 8:19:58 AM |
| Type | Journal Article |
|---|---|
| Author | Nina Wilson |
| Author | Katie Biggs |
| Author | Sarah Bowden |
| Author | Julia Brown |
| Author | Munyaradzi Dimairo |
| Author | Laura Flight |
| Author | Jamie Hall |
| Author | Anna Hockaday |
| Author | Thomas Jaki |
| Author | Rachel Lowe |
| Author | Caroline Murphy |
| Author | Philip Pallmann |
| Author | Mark A. Pilling |
| Author | Claire Snowdon |
| Author | Matthew R. Sydes |
| Author | Sofía S. Villar |
| Author | Christopher J. Weir |
| Author | Jessica Welburn |
| Author | Christina Yap |
| Author | Rebecca Maier |
| Author | Helen Hancock |
| Author | James M. S. Wason |
| URL | https://doi.org/10.1186/s12916-021-02124-z |
| Volume | 19 |
| Issue | 1 |
| Pages | 251 |
| Publication | BMC Medicine |
| ISSN | 1741-7015 |
| Date | October 26, 2021 |
| Journal Abbr | BMC Medicine |
| DOI | 10.1186/s12916-021-02124-z |
| Accessed | 10/26/2021, 9:32:32 AM |
| Library Catalog | BioMed Central |
| Abstract | Adaptive designs offer great promise in improving the efficiency and patient-benefit of clinical trials. An important barrier to further increased use is a lack of understanding about which additional resources are required to conduct a high-quality adaptive clinical trial, compared to a traditional fixed design. |
| Short Title | Costs and staffing resource requirements for adaptive clinical trials |
| Date Added | 10/26/2021, 9:32:32 AM |
| Modified | 10/26/2021, 9:32:56 AM |
| Type | Journal Article |
|---|---|
| Author | Laura Freijeiro-González |
| Author | Manuel Febrero-Bande |
| Author | Wenceslao González-Manteiga |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1111/insr.12469 |
| Volume | n/a |
| Issue | n/a |
| Publication | International Statistical Review |
| ISSN | 1751-5823 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/insr.12469 |
| DOI | 10.1111/insr.12469 |
| Accessed | 10/26/2021, 9:24:16 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | The limitations of the well-known LASSO regression as a variable selector are tested when there exists dependence structures among covariates. We analyse both the classic situation with n ≥ p and the high dimensional framework with p > n. Known restrictive properties of this methodology to guarantee optimality, as well as inconveniences in practice, are analysed and tested by means of an extensive simulation study. Examples of these drawbacks are showed making use of different dependence scenarios. In order to search for improvements, a broad comparison with LASSO derivatives and alternatives is carried out. Eventually, we give some guidance about what procedures work best in terms of the considered data nature. |
| Date Added | 10/26/2021, 9:24:16 AM |
| Modified | 10/26/2021, 9:25:08 AM |
| 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, 10:08:27 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, 10:08:27 AM |
| Modified | 10/21/2021, 10:11:10 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.
| Type | Journal Article |
|---|---|
| Author | James R. Carpenter |
| Author | Melanie Smuk |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.202000196 |
| Volume | 63 |
| Issue | 5 |
| Pages | 915-947 |
| Publication | Biometrical Journal |
| ISSN | 1521-4036 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202000196 |
| DOI | 10.1002/bimj.202000196 |
| Accessed | 10/21/2021, 8:58:56 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Missing data are ubiquitous in medical research, yet there is still uncertainty over when restricting to the complete records is likely to be acceptable, when more complex methods (e.g. maximum likelihood, multiple imputation and Bayesian methods) should be used, how they relate to each other and the role of sensitivity analysis. This article seeks to address both applied practitioners and researchers interested in a more formal explanation of some of the results. For practitioners, the framework, illustrative examples and code should equip them with a practical approach to address the issues raised by missing data (particularly using multiple imputation), alongside an overview of how the various approaches in the literature relate. In particular, we describe how multiple imputation can be readily used for sensitivity analyses, which are still infrequently performed. For those interested in more formal derivations, we give outline arguments for key results, use simple examples to show how methods relate, and references for full details. The ideas are illustrated with a cohort study, a multi-centre case control study and a randomised clinical trial. |
| Short Title | Missing data |
| Date Added | 10/21/2021, 8:58:56 AM |
| Modified | 10/21/2021, 9:06:36 AM |
| Type | Journal Article |
|---|---|
| Author | Edgar Brunner |
| Author | Frank Konietschke |
| Author | Arne C. Bathke |
| Author | Markus Pauly |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1111/insr.12418 |
| Volume | 89 |
| Issue | 2 |
| Pages | 349-366 |
| Publication | International Statistical Review |
| ISSN | 1751-5823 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/insr.12418 |
| DOI | 10.1111/insr.12418 |
| Accessed | 10/13/2021, 12:48:13 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Rank-based inference methods are applied in various disciplines, typically when procedures relying on standard normal theory are not justifiable. Various specific rank-based methods have been developed for two and more samples and also for general factorial designs (e.g. Kruskal–Wallis test or Akritas–Arnold–Brunner test). It is the aim of the present paper (1) to demonstrate that traditional rank procedures for several samples or general factorial designs may lead to surprising results in case of unequal sample sizes as compared with equal sample sizes, (2) to explain why this is the case and (3) to provide a way to overcome these disadvantages. Theoretical investigations show that the surprising results can be explained by considering the non-centralities of the test statistics, which may be non-zero for the usual rank-based procedures in case of unequal sample sizes, while they may be equal to 0 in case of equal sample sizes. A simple solution is to consider unweighted relative effects instead of weighted relative effects. The former effects are estimated by means of the so-called pseudo-ranks, while the usual ranks naturally lead to the latter effects. A real data example illustrates the practical meaning of the theoretical discussions. |
| Date Added | 10/13/2021, 12:48:14 PM |
| Modified | 10/13/2021, 12:49:09 PM |
| Type | Journal Article |
|---|---|
| Author | Ana W. Capuano |
| Author | Jeffrey D. Dawson |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.5689 |
| Volume | 32 |
| Issue | 13 |
| Pages | 2250-2261 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2013 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.5689 |
| DOI | 10.1002/sim.5689 |
| Accessed | 10/8/2021, 5:32:17 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Ordinal data appear in a wide variety of scientific fields. These data are often analyzed using ordinal logistic regression models that assume proportional odds. When this assumption is not met, it may be possible to capture the lack of proportionality using a constrained structural relationship between the odds and the cut-points of the ordinal values. We consider a trend odds version of this constrained model, wherein the odds parameter increases or decreases in a monotonic manner across the cut-points. We demonstrate algebraically and graphically how this model is related to latent logistic, normal, and exponential distributions. In particular, we find that scale changes in these potential latent distributions are consistent with the trend odds assumption, with the logistic and exponential distributions having odds that increase in a linear or nearly linear fashion. We show how to fit this model using SAS Proc NLMIXED and perform simulations under proportional odds and trend odds processes. We find that the added complexity of the trend odds model gives improved power over the proportional odds model when there are moderate to severe departures from proportionality. A hypothetical data set is used to illustrate the interpretation of the trend odds model, and we apply this model to a swine influenza example wherein the proportional odds assumption appears to be violated. Copyright © 2012 John Wiley & Sons, Ltd. |
| Date Added | 10/8/2021, 5:32:19 PM |
| Modified | 10/8/2021, 5:32:19 PM |
| Type | Journal Article |
|---|---|
| Author | Georg Zimmermann |
| Author | Edgar Brunner |
| Author | Werner Brannath |
| Author | Martin Happ |
| Author | Arne C. Bathke |
| URL | https://doi.org/10.1080/00031305.2021.1972836 |
| Volume | 0 |
| Issue | 0 |
| Pages | 1-7 |
| Publication | The American Statistician |
| ISSN | 0003-1305 |
| Date | September 3, 2021 |
| Extra | Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00031305.2021.1972836 |
| DOI | 10.1080/00031305.2021.1972836 |
| Accessed | 10/6/2021, 10:19:43 PM |
| Library Catalog | Taylor and Francis+NEJM |
| Abstract | Rank-based methods are frequently used in the life sciences, and in the empirical sciences in general. Among the best-known examples of nonparametric rank-based tests are the Wilcoxon-Mann-Whitney test and the Kruskal–Wallis test. However, recently, potential pitfalls and paradoxical results pertaining to the use of traditional rank-based procedures for more than two samples have been highlighted, and the so-called pseudo-ranks have been proposed as a remedy for this type of problems. The aim of the present article is twofold: First, we show that pseudo-ranks might also behave counterintuitively when splitting up groups. Second, since the use of pseudo-ranks leads to a slightly different interpretation of the results, we provide some guidance regarding the decision for one or the other approach, in particular with respect to interpretability and generalizability of the findings. It turns out that the choice of the reference distribution, to which the individual groups are compared, is crucial. The practically relevant implications of these aspects are illustrated by a discussion of a dataset from epilepsy research. Summing up, one should decide based on thorough case-by-case considerations whether ranks or pseudo-ranks are appropriate. |
| Short Title | Pseudo-Ranks |
| Date Added | 10/6/2021, 10:19:43 PM |
| Modified | 10/6/2021, 10:20:26 PM |
| Type | Journal Article |
|---|---|
| Author | Hana Šinkovec |
| Author | Georg Heinze |
| Author | Rok Blagus |
| Author | Angelika Geroldinger |
| URL | https://doi.org/10.1186/s12874-021-01374-y |
| Volume | 21 |
| Issue | 1 |
| Pages | 199 |
| Publication | BMC Medical Research Methodology |
| ISSN | 1471-2288 |
| Date | September 30, 2021 |
| Journal Abbr | BMC Medical Research Methodology |
| DOI | 10.1186/s12874-021-01374-y |
| Accessed | 10/6/2021, 3:14:01 PM |
| Library Catalog | BioMed Central |
| Abstract | For finite samples with binary outcomes penalized logistic regression such as ridge logistic regression has the potential of achieving smaller mean squared errors (MSE) of coefficients and predictions than maximum likelihood estimation. There is evidence, however, that ridge logistic regression can result in highly variable calibration slopes in small or sparse data situations. |
| Date Added | 10/6/2021, 3:14:01 PM |
| Modified | 10/6/2021, 3:14:29 PM |
| 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, 8:01:01 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, 8:01:01 AM |
| Modified | 10/3/2021, 7:28:11 PM |
| Type | Journal Article |
|---|---|
| Author | Tra My Pham |
| Author | Ian R. White |
| Author | Brennan C. Kahan |
| Author | Tim P. Morris |
| Author | Simon J. Stanworth |
| Author | Gordon Forbes |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9203 |
| 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.9203 |
| DOI | 10.1002/sim.9203 |
| Accessed | 9/30/2021, 12:17:20 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Composite endpoints are commonly used to define primary outcomes in randomized controlled trials. A participant may be classified as meeting the endpoint if they experience an event in one or several components (eg, a favorable outcome based on a composite of being alive and attaining negative culture results in trials assessing tuberculosis treatments). Partially observed components that are not missing simultaneously complicate the analysis of the composite endpoint. An intuitive strategy frequently used in practice for handling missing values in the components is to derive the values of the composite endpoint from observed components when possible, and exclude from analysis participants whose composite endpoint cannot be derived. Alternatively, complete record analysis (CRA) (excluding participants with any missing components) or multiple imputation (MI) can be used. We compare a set of methods for analyzing a composite endpoint with partially observed components mathematically and by simulation, and apply these methods in a reanalysis of a published trial (TOPPS). We show that the derived composite endpoint can be missing not at random even when the components are missing completely at random. Consequently, the treatment effect estimated from the derived endpoint is biased while CRA results without the derived endpoint are valid. Missing at random mechanisms require MI of the components. We conclude that, although superficially attractive, deriving the composite endpoint from observed components should generally be avoided. Despite the potential risk of imputation model mis-specification, MI of missing components is the preferred approach in this study setting. |
| Date Added | 9/30/2021, 12:17:20 PM |
| Modified | 9/30/2021, 12:17:53 PM |
| Type | Journal Article |
|---|---|
| Author | Junjing Lin |
| Author | Margaret Gamalo-Siebers |
| Author | Ram Tiwari |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2172 |
| Volume | n/a |
| Issue | n/a |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2172 |
| DOI | 10.1002/pst.2172 |
| Accessed | 9/30/2021, 12:15:42 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | In many orphan diseases and pediatric indications, the randomized controlled trials may be infeasible because of their size, duration, and cost. Leveraging information on the control through a prior can potentially reduce sample size. However, unless an objective prior is used to impose complete ignorance for the parameter being estimated, it results in biased estimates and inflated type-I error. Hence, it is essential to assess both the confirmatory and supplementary knowledge available during the construction of the prior to avoid “cherry-picking” advantageous information. For this purpose, propensity score methods are employed to minimize selection bias by weighting supplemental control subjects according to their similarity in terms of pretreatment characteristics to the subjects in the current trial. The latter can be operationalized through a proposed measure of overlap in propensity-score distributions. In this paper, we consider single experimental arm in the current trial and the control arm is completely borrowed from the supplemental data. The simulation experiments show that the proposed method reduces prior and data conflict and improves the precision of the of the average treatment effect. |
| Date Added | 9/30/2021, 12:15:42 PM |
| Modified | 9/30/2021, 12:16:11 PM |
| Type | Journal Article |
|---|---|
| Author | Aris Perperoglou |
| Author | Willi Sauerbrei |
| Author | Michal Abrahamowicz |
| Author | Matthias Schmid |
| URL | https://doi.org/10.1186/s12874-019-0666-3 |
| Volume | 19 |
| Issue | 1 |
| Pages | 46 |
| Publication | BMC Medical Research Methodology |
| ISSN | 1471-2288 |
| Date | March 6, 2019 |
| Journal Abbr | BMC Medical Research Methodology |
| DOI | 10.1186/s12874-019-0666-3 |
| Accessed | 9/29/2021, 9:46:20 PM |
| Library Catalog | BioMed Central |
| Abstract | With progress on both the theoretical and the computational fronts the use of spline modelling has become an established tool in statistical regression analysis. An important issue in spline modelling is the availability of user friendly, well documented software packages. Following the idea of the STRengthening Analytical Thinking for Observational Studies initiative to provide users with guidance documents on the application of statistical methods in observational research, the aim of this article is to provide an overview of the most widely used spline-based techniques and their implementation in R. |
| Date Added | 9/29/2021, 9:46:20 PM |
| Modified | 9/29/2021, 9:46:50 PM |
| Type | Journal Article |
|---|---|
| Author | Tomasz Burzykowski |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2169 |
| Volume | n/a |
| Issue | n/a |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2169 |
| DOI | 10.1002/pst.2169 |
| Accessed | 9/27/2021, 2:17:26 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | The accelerated failure-time (AFT) model has been long recognized as a useful alternative to the proportional-hazards (PH) model. Semi-parametric AFT model has been known since 1981. Its use has been hampered by the difficulty in solving the estimating equations for the model's coefficients. In recent years, however, important developments have taken place regarding the methods of solving the equations. In this article, we briefly review the developments, focusing mainly on rank-based estimation. We conduct a simulation study that directly focuses on the applicability of the model in the context of (cancer) clinical trials. We also investigate the robustness of the AFT model to the omission of covariates. Finally, we conduct a meta-analysis of multiple clinical trials in gastric cancer to illustrate the benefits of the use of the model in practice. |
| Short Title | Semi-parametric accelerated failure-time model |
| Date Added | 9/27/2021, 2:17:26 PM |
| Modified | 9/27/2021, 2:17:50 PM |
| Type | Journal Article |
|---|---|
| Author | Lu Mao |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13570 |
| Volume | n/a |
| Issue | n/a |
| Publication | Biometrics |
| ISSN | 1541-0420 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13570 |
| DOI | 10.1111/biom.13570 |
| Accessed | 9/27/2021, 2:11:44 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | The restricted mean time in favor (RMT-IF) of treatment is a nonparametric effect size for complex life history data. It is defined as the net average time the treated spend in a more favorable state than the untreated over a pre-specified time window. It generalizes the familiar restricted mean survival time from the two-state life-death model to account for intermediate stages in disease progression. The overall estimand can be additively decomposed into stage-wise effects, with the standard restricted mean survival time as a component. Alternate expressions of the overall and stage-wise estimands as integrals of the marginal survival functions for a sequence of landmark transitioning events allow them to be easily estimated by plug-in Kaplan–Meier estimators. The dynamic profile of the estimated treatment effects as a function of follow-up time can be visualized using a multilayer, cone-shaped “bouquet plot”. Simulation studies under realistic settings show that the RMT-IF meaningfully and accurately quantifies the treatment effect and outperforms traditional tests on time to the first event in statistical efficiency thanks to its fuller utilization of patient data. The new methods are illustrated on a colon cancer trial with relapse and death as outcomes and a cardiovascular trial with recurrent hospitalizations and death as outcomes. The R-package rmt implements the proposed methodology and is publicly available from the Comprehensive R Archive Network (CRAN). This article is protected by copyright. All rights reserved |
| Date Added | 9/27/2021, 2:11:44 PM |
| Modified | 9/27/2021, 2:13:01 PM |
The author's notation is gorgeous.
"According to ICH-E9(R1) Addendum, such intercurrent events should be accounted for as part of the outcome rather than censoring' (mentions "discontinuation of treatment due to side effect, that terminates the follow-up and prevents subsequent events from being observed."
Bouquet plot
"Overall, patients undergoing exercise training gain an average of 5.1 months in a more favorable state compared with those under usual care only. This gain comprises an additional 2.9 months of survival time ... and an additional 2.2 months with fewer hospitalizations among the living"
| Type | Journal Article |
|---|---|
| Author | Paul Aubel |
| Author | Marine Antigny |
| Author | Ronan Fougeray |
| Author | Frédéric Dubois |
| Author | Gaëlle Saint-Hilary |
| URL | http://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9186 |
| 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.9186 |
| DOI | 10.1002/sim.9186 |
| Accessed | 9/21/2021, 10:13:44 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | In clinical trials with time-to-event outcome as the primary endpoint, the end of study date is often based on the number of observed events, which drives the statistical power and the sample size calculation. It is of great value for study sponsors to have a good understanding of the recruitment process and the event milestones to manage the logistical tasks, which require a considerable amount of resources. The objective of the proposed statistical approach is to predict, as accurately as possible, the timing of an analysis planned once a target number of events is collected. The method takes into account the enrollment, the time to event, and the time to censor processes, using Weibull models in a Bayesian framework. We also consider a possible delay in the event reporting by the investigators, and covariates may also be included. Several metrics can be obtained, such as the probability of study completion at specific timepoints or the credible interval of the date of study completion. The approach was applied to oncology trials, with progression-free survival as primary outcome. A retrospective analysis shows the accuracy of the approach on these examples, as well as the benefit of updating the predictive probability of study completion as data are accumulating or new information becomes available. We also evaluated the performances of the proposed method in a comprehensive simulation study. |
| Date Added | 9/21/2021, 10:13:44 AM |
| Modified | 9/21/2021, 10:14:33 AM |
| Type | Journal Article |
|---|---|
| Author | Mohammad Ali Mansournia |
| Author | Mahyar Etminan |
| Author | Goodarz Danaei |
| Author | Jay S. Kaufman |
| Author | Gary Collins |
| URL | https://www.bmj.com/content/359/bmj.j4587 |
| 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 |
| Volume | 359 |
| Pages | j4587 |
| Publication | BMJ |
| ISSN | 0959-8138, 1756-1833 |
| Date | 2017/10/16 |
| Extra | Publisher: British Medical Journal Publishing Group Section: Research Methods & Reporting PMID: 29038130 |
| Journal Abbr | BMJ |
| DOI | 10.1136/bmj.j4587 |
| Accessed | 9/15/2021, 12:47:43 PM |
| Library Catalog | www.bmj.com |
| Language | en |
| Abstract | <p>Many exposures of epidemiological interest are time varying, and the values of potential confounders may change over time leading to time varying confounding. The aim of many longitudinal studies is to estimate the causal effect of a time varying exposure on an outcome that requires adjusting for time varying confounding. Time varying confounding affected by previous exposure often occurs in practice, but it is usually adjusted for by using conventional analytical methods such as time dependent Cox regression, random effects models, or generalised estimating equations, which are known to provide biased effect estimates in this setting. This article explains time varying confounding affected by previous exposure and outlines three causal methods proposed to appropriately adjust for this potential bias: inverse-probability-of-treatment weighting, the parametric G formula, and G estimation<i>.</i></p> |
| Date Added | 9/15/2021, 12:47:43 PM |
| Modified | 9/15/2021, 12:48:46 PM |
| Type | Journal Article |
|---|---|
| Author | Sander Greenland |
| URL | https://doi.org/10.1080/00031305.2018.1529625 |
| Volume | 73 |
| Issue | sup1 |
| Pages | 106-114 |
| Publication | The American Statistician |
| ISSN | 0003-1305 |
| Date | March 29, 2019 |
| Extra | Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00031305.2018.1529625 |
| DOI | 10.1080/00031305.2018.1529625 |
| Accessed | 9/15/2021, 12:42:10 PM |
| Library Catalog | Taylor and Francis+NEJM |
| Abstract | The present note explores sources of misplaced criticisms of P-values, such as conflicting definitions of “significance levels” and “P-values” in authoritative sources, and the consequent misinterpretation of P-values as error probabilities. It then discusses several properties of P-values that have been presented as fatal flaws: That P-values exhibit extreme variation across samples (and thus are “unreliable”), confound effect size with sample size, are sensitive to sample size, and depend on investigator sampling intentions. These properties are often criticized from a likelihood or Bayesian framework, yet they are exactly the properties P-values should exhibit when they are constructed and interpreted correctly within their originating framework. Other common criticisms are that P-values force users to focus on irrelevant hypotheses and overstate evidence against those hypotheses. These problems are not however properties of P-values but are faults of researchers who focus on null hypotheses and overstate evidence based on misperceptions that p = 0.05 represents enough evidence to reject hypotheses. Those problems are easily seen without use of Bayesian concepts by translating the observed P-value p into the Shannon information (S-value or surprisal) –log2(p). |
| Short Title | Valid P-Values Behave Exactly as They Should |
| Date Added | 9/15/2021, 12:42:10 PM |
| Modified | 9/15/2021, 12:42:38 PM |
A P-value is not an error probability (except in a useless hypothetical sense)
P-values are not calibrated to amount of evidence, but S-values are.
Fisher uses "null hypothesis" for any tested hypothesis, which has caused a great deal of confusion. Neyman preferred the term the targeted or tested hypothesis.
"The S-value reveals that no parameter value inside a 95% confidence interval has more than 4.3 bits of information against it, supporting recommendations to view the interval interior as a region of hypotheses highly compatible with the data, rather than overconfidently viewing its exterior as a region ruled out by the data."
| Type | Journal Article |
|---|---|
| Author | Edgar Brunner |
| Author | Marc Vandemeulebroecke |
| Author | Tobias Mütze |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8967 |
| Volume | 40 |
| Issue | 14 |
| Pages | 3367-3384 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.8967 |
| DOI | 10.1002/sim.8967 |
| Accessed | 9/15/2021, 12:25:02 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | The win ratio, a recently proposed measure for comparing the benefit of two treatment groups, allows ties in the data but ignores ties in the inference. In this article, we highlight some difficulties that this can lead to, and we propose to focus on the win odds instead, a modification of the win ratio which takes ties into account. We construct hypothesis tests and confidence intervals for the win odds, and we investigate their properties through simulations and in a case study. We conclude that the win odds should be preferred over the win ratio. |
| Short Title | Win odds |
| Date Added | 9/15/2021, 12:25:02 PM |
| Modified | 9/15/2021, 12:25:39 PM |
Nice information about Wilcoxon test and ties.
Win ratio provides a larger effect size with more times, even though more ties decreases its precision. The win odds is more intuitive and interpretable.
| Type | Journal Article |
|---|---|
| Author | Andrew Gelman |
| Author | Aki Vehtari |
| Author | Daniel Simpson |
| Author | Charles C. Margossian |
| Author | Bob Carpenter |
| Author | Yuling Yao |
| Author | Lauren Kennedy |
| Author | Jonah Gabry |
| Author | Paul-Christian Bürkner |
| Author | Martin Modrák |
| URL | http://arxiv.org/abs/2011.01808 |
| Publication | arXiv:2011.01808 [stat] |
| Date | 2020-11-03 |
| Extra | arXiv: 2011.01808 |
| Accessed | 9/8/2021, 1:13:48 PM |
| Library Catalog | arXiv.org |
| Abstract | The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilistic programming languages make it easier to specify and fit Bayesian models, but this still leaves us with many options regarding constructing, evaluating, and using these models, along with many remaining challenges in computation. Using Bayesian inference to solve real-world problems requires not only statistical skills, subject matter knowledge, and programming, but also awareness of the decisions made in the process of data analysis. All of these aspects can be understood as part of a tangled workflow of applied Bayesian statistics. Beyond inference, the workflow also includes iterative model building, model checking, validation and troubleshooting of computational problems, model understanding, and model comparison. We review all these aspects of workflow in the context of several examples, keeping in mind that in practice we will be fitting many models for any given problem, even if only a subset of them will ultimately be relevant for our conclusions. |
| Date Added | 9/8/2021, 1:13:48 PM |
| Modified | 9/8/2021, 1:14:06 PM |
Comment: 77 pages, 35 figures
| Type | Journal Article |
|---|---|
| Author | Julie Kjærulff Furberg |
| Author | Søren Rasmussen |
| Author | Per Kragh Andersen |
| Author | Henrik Ravn |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2167 |
| Volume | n/a |
| Issue | n/a |
| Publication | Pharmaceutical Statistics |
| ISSN | 1539-1612 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pst.2167 |
| DOI | 10.1002/pst.2167 |
| Accessed | 9/8/2021, 12:44:51 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Analysis of recurrent events is becoming increasingly popular for understanding treatment effects in randomised controlled trials. The analysis of recurrent events can improve efficiency and capture disease burden compared to standard time-to-first event analyses. However, the added knowledge about the multi-state process comes at the cost of modelling complexity. High mortality rates can complicate matters even more. A case study using data from a randomised controlled trial, LEADER, is presented to highlight interpretation of common methods as well as potential pitfalls when analysing recurrent events in the presence of a competing risk. The presented methods either target features of the underlying intensity functions or marginal traits of a multi-state process which includes terminal events or not. In particular, approaches to handle death as a part of an event and as a competing risk are discussed. A new method targeting the marginal mean function for a composite endpoint, which includes both death as a component and as a competing risk, will be introduced. Finally, recommendations for how to capture meaningful treatment effects in randomised controlled trials when analysing recurrent and terminal events will be made. |
| Date Added | 9/8/2021, 12:44:51 PM |
| Modified | 9/8/2021, 12:45:48 PM |
| 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, 12:43:02 PM |
| 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, 12:43:02 PM |
| Modified | 9/8/2021, 12:43:43 PM |
| Type | Journal Article |
|---|---|
| Author | Gisèle Nakhlé |
| Author | James M. Brophy |
| Author | Christel Renoux |
| Author | Paul Khairy |
| Author | Patrick Bélisle |
| Author | Jacques LeLorier |
| URL | https://www.jclinepi.com/article/S0895-4356(21)00282-1/abstract |
| Volume | 0 |
| Issue | 0 |
| Publication | Journal of Clinical Epidemiology |
| ISSN | 0895-4356, 1878-5921 |
| Date | 2021/09/07 |
| Extra | Publisher: Elsevier |
| Journal Abbr | Journal of Clinical Epidemiology |
| DOI | 10.1016/j.jclinepi.2021.09.002 |
| Accessed | 9/8/2021, 12:34:40 PM |
| Library Catalog | www.jclinepi.com |
| Language | English |
| Abstract | Over the last two decades, Bayesian methods have gained popularity and their implementations have widened in statistical sciences and applied fields. From a healthcare perspective, regulatory agencies are now accepting Bayesian approaches for earlier phases of drug development [1,2] and comparative effectiveness research [3,4]. At the drug development level, Bayesian adaptive analytical approaches have been particularly attractive for achieving greater efficiency in reducing sample size, time and cost of trials [5]. |
| Short Title | Domperidone Increases Harmful Cardiac Events in Parkinson's Disease |
| Date Added | 9/8/2021, 12:34:40 PM |
| Modified | 9/8/2021, 12:35:12 PM |
| Type | Journal Article |
|---|---|
| Author | Ricardo Sanchez |
| Author | Beth Ann Griffin |
| Author | Joseph Pane |
| Author | Daniel F. McCaffrey |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9169 |
| 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.9169 |
| DOI | 10.1002/sim.9169 |
| Accessed | 9/7/2021, 4:38:29 PM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | The world is becoming increasingly complex, both in terms of the rich sources of data we have access to and the statistical and computational methods we can use on data. These factors create an ever-increasing risk for errors in code and the sensitivity of findings to data preparation and the execution of complex statistical and computing methods. The consequences of coding and data mistakes can be substantial. In this paper, we describe the key steps for implementing a code quality assurance (QA) process that researchers can follow to improve their coding practices throughout a project to assure the quality of the final data, code, analyses, and results. These steps include: (i) adherence to principles for code writing and style that follow best practices; (ii) clear written documentation that describes code, workflow, and key analytic decisions; (iii) careful version control; (iv) good data management; and (v) regular testing and review. Following these steps will greatly improve the ability of a study to assure results are accurate and reproducible. The responsibility for code QA falls not only on individual researchers but institutions, journals, and funding agencies as well. |
| Date Added | 9/7/2021, 4:38:29 PM |
| Modified | 9/7/2021, 4:39:29 PM |
| Type | Journal Article |
|---|---|
| Author | Karla Hemming |
| Author | Monica Taljaard |
| Author | Charles Weijer |
| Author | Andrew B. Forbes |
| URL | https://www.bmj.com/content/371/bmj.m3800 |
| 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 |
| Volume | 371 |
| Pages | m3800 |
| Publication | BMJ |
| ISSN | 1756-1833 |
| Date | 2020/11/04 |
| Extra | Publisher: British Medical Journal Publishing Group Section: Research Methods & Reporting PMID: 33148538 |
| Journal Abbr | BMJ |
| DOI | 10.1136/bmj.m3800 |
| Accessed | 9/5/2021, 5:18:41 PM |
| Library Catalog | www.bmj.com |
| Language | en |
| Abstract | <p>Many treatments are adopted into clinical practice without a solid evidence base and might be used heterogeneously across settings. Rigorous randomised controlled trials are therefore needed to inform decisions about the comparative effectiveness of treatments in common use. The mainstay of comparative effectiveness research is pragmatic trial design, which emphasises broad eligibility criteria, simple logistics, routinely collected outcome data, and cost efficient designs. Although treatment differences at the individual level might be small, they can become important when aggregated across large populations. To detect these small differences, very large trials are often required. In multiple period, cluster randomised, crossover trials, the study design randomises clusters (eg, hospitals) to exposure to different interventions in a randomly determined order, and is an attractive design for comparative effectiveness research. The trial design can be highly statistically efficient, compared with other competing designs, and can have many logistical advantages. Several prominent examples of this trial design have been published recently, yet practical guidance is lacking on how best to design these trials to ensure that they provide robust evidence. Some considerations include how to determine the frequency and number of crossovers, the importance of a time balanced design, how to determine the required sample size, and how to analyse appropriately. The justification for using this design (over a design randomising on the level of individual patients) also raises ethical concerns when used to evaluate individual level interventions without the prospective informed consent of individual participants. In this article, we outline the key methodological and ethical requirements needed for the robust design of multiple period, cluster randomised, crossover trials.</p> |
| Date Added | 9/5/2021, 5:18:41 PM |
| Modified | 9/5/2021, 5:19:46 PM |
| Type | Journal Article |
|---|---|
| Author | Stephen Senn |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.4780122412 |
| Volume | 12 |
| Issue | 24 |
| Pages | 2367-2375 |
| Publication | Statistics in Medicine |
| ISSN | 1097-0258 |
| Date | 1993 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.4780122412 |
| DOI | 10.1002/sim.4780122412 |
| Accessed | 9/4/2021, 9:57:56 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | A simple model is used to investigate the relevance of ‘competence’ to active control equivalence studies )ACES(. It is shown that to the extent that such trials are successful the results of such trials must raise doubts regarding their competence. ACES are thus more problematic than classical clinical trials and the problems with such studies cannot be solved simply by exchanging the usual roles of null and alternative hypotheses. |
| Date Added | 9/4/2021, 9:57:56 AM |
| Modified | 9/4/2021, 9:58:36 AM |
| Type | Book |
|---|---|
| Author | Street, Deborah J |
| Author | Leonie Burgess |
| URL | https://onlinelibrary-wiley-com.proxy.library.vanderbilt.edu/doi/book/10.1002/9780470148563 |
| Publisher | Wiley and Sons |
| ISBN | 978-0-470-05332-4 |
| Date | 2007 |
| Date Added | 8/31/2021, 4:37:57 PM |
| Modified | 8/31/2021, 4:40:12 PM |
| Type | Journal Article |
|---|---|
| Author | Deborah J. Street |
| Author | Leonie Burgess |
| Author | Jordan J. Louviere |
| URL | https://www.sciencedirect.com/science/article/pii/S0167811605000510 |
| Volume | 22 |
| Issue | 4 |
| Pages | 459-470 |
| Publication | International Journal of Research in Marketing |
| ISSN | 0167-8116 |
| Date | December 1, 2005 |
| Journal Abbr | International Journal of Research in Marketing |
| DOI | 10.1016/j.ijresmar.2005.09.003 |
| Accessed | 8/31/2021, 4:30:19 PM |
| Library Catalog | ScienceDirect |
| Language | en |
| Abstract | In this paper we compare a number of common strategies for constructing discrete choice experiments. Two of the strategies, including one based on theoretical constructions for optimal discrete choice experiments, produce designs that are better than those that come about from random grouping and from using the LMA construction. A simple account of this theoretical construction is given. |
| Short Title | Quick and easy choice sets |
| Date Added | 8/31/2021, 4:30:19 PM |
| Modified | 8/31/2021, 4:31:05 PM |
| Type | Journal Article |
|---|---|
| Author | Matthew Quaife |
| Author | Fern Terris-Prestholt |
| Author | Gian Luca Di Tanna |
| Author | Peter Vickerman |
| URL | https://doi.org/10.1007/s10198-018-0954-6 |
| Volume | 19 |
| Issue | 8 |
| Pages | 1053-1066 |
| Publication | The European Journal of Health Economics |
| ISSN | 1618-7601 |
| Date | 2018-11-01 |
| Journal Abbr | Eur J Health Econ |
| DOI | 10.1007/s10198-018-0954-6 |
| Accessed | 8/31/2021, 4:27:18 PM |
| Library Catalog | Springer Link |
| Language | en |
| Abstract | Discrete choice experiments (DCEs) are economic tools that elicit the stated preferences of respondents. Because of their increasing importance in informing the design of health products and services, it is critical to understand the extent to which DCEs give reliable predictions outside of the experimental context. We systematically reviewed the literature of published DCE studies comparing predictions to choices made in reality; we extracted individual-level data to estimate a bivariate mixed-effects model of pooled sensitivity and specificity. Eight studies met the inclusion criteria, and six of these gave sufficient data for inclusion in a meta-analysis. Pooled sensitivity and specificity estimates were 88% (95% CI 81, 92%) and 34% (95% CI 23, 46%), respectively, and the area under the SROC curve (AUC) was 0.60 (95% CI 0.55, 0.64). Results indicate that DCEs can produce reasonable predictions of health-related behaviors. There is a great need for future research on the external validity of DCEs, particularly empirical studies assessing predicted and revealed preferences of a representative sample of participants. |
| Short Title | How well do discrete choice experiments predict health choices? |
| Date Added | 8/31/2021, 4:27:18 PM |
| Modified | 8/31/2021, 4:27:57 PM |