| 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.
| 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 |
| 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 |
| 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.
| 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.
| 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.
| 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.
| 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.
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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)
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |