Journal ArticleDOI
Joint analysis of longitudinal data comprising repeated measures and times to events
Jane Xu,Scott L. Zeger +1 more
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TLDR
In this article, a general model for the joint analysis of [T, YIX] and other related functionals by using the relevant information in both T and Y is presented.Abstract:
Summary. In biomedical and public health research, both repeated measures of biomarkers Y as well as times Tto key clinical events are often collected for a subject. The scientific question is how the distribution of the responses [T, YIX] changes with covariates X [TIX] may be the focus of the estimation where Ycan be used as a surrogate for T. Alternatively, Tmay be the time to drop-out in a study in which [YIX] is the target for estimation. Also, the focus of a study might be on the effects of covariates X on both T and Y or on some underlying latent variable which is thought to be manifested in the observable outcomes. In this paper, we present a general model for the joint analysis of [T, YIX] and apply the model to estimate [TIX] and other related functionals by using the relevant information in both Tand Y We adopt a latent variable formulation like that of Fawcett and Thomas and use it to estimate several quantities of clinical relevance to determine the efficacy of a treatment in a clinical trial setting. We use a Markov chain Monte Carlo algorithm to estimate the model's parameters. We illustrate the methodology with an analysis of data from a clinical trial comparing risperidone with a placebo for the treatment of schizophrenia.read more
Citations
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Book ChapterDOI
Random Effects Models for Longitudinal Data
TL;DR: This chapter gives an overview of frequently used mixed models for continuous as well as discrete longitudinal data, with emphasis on model formulation and parameter interpretation.
Journal ArticleDOI
JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data
TL;DR: This paper presents the R package JM, a package JM that fits joint models for longitudinal and time-to-event data, and describes its use in longitudinal studies.
Journal ArticleDOI
Considerations in the evaluation of surrogate endpoints in clinical trials. summary of a National Institutes of Health workshop.
Victor De Gruttola,Pamela Clax,David L. DeMets,Gregory J. Downing,Susan S. Ellenberg,Lawrence M. Friedman,Mitchell H. Gail,Ross L. Prentice,Janet Wittes,Scott L. Zeger +9 more
TL;DR: Recommendations from a National Institutes of Health Workshop on methods for evaluating the use of surrogate endpoints in clinical trials included a strong recommendation for increased training of quantitative scientists in biologic research as well as in statistical methods and modeling to ensure that there will be an adequate workforce to meet future research needs.
Journal ArticleDOI
Missing data methods in longitudinal studies: a review
TL;DR: Elements of taxonomy include: missing data patterns, mechanisms, and modeling frameworks; inferential paradigms; and sensitivity analysis frameworks; andensitivity analysis frameworks are described in detail.
Journal ArticleDOI
Basic Concepts and Methods for Joint Models of Longitudinal and Survival Data
TL;DR: An introductory overview on joint modeling for longitudinal and survival data is given and a general discussion of a broad range of issues that arise in the design and analysis of clinical trials using joint models are presented.
References
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Book
Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
Journal ArticleDOI
Generalized Linear Models
TL;DR: This is the rst book on generalized linear models written by authors not mostly associated with the biological sciences, and it is thoroughly enjoyable to read.
Regression models and life tables (with discussion
TL;DR: The drum mallets disclosed in this article are adjustable, by the percussion player, as to balance, overall weight, head characteristics and tone production of the mallet, whereby the adjustment can be readily obtained.
Book
Analysis of longitudinal data
TL;DR: In this paper, a generalized linear model for longitudinal data and transition models for categorical data are presented. But the model is not suitable for categric data and time dependent covariates are not considered.
Journal ArticleDOI
Statistical Analysis With Missing Data
TL;DR: Generalized Estimating Equations is a good introductory book for analyzing continuous and discrete correlated data using GEE methods and provides good guidance for analyzing correlated data in biomedical studies and survey studies.