NDLI: Joint Analysis of Survival Time and Longitudinal Categorical Outcomes

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Quantifying Immune Response to Influenza Virus Infection via Multivariate Nonlinear ODE Models with Partially Observed State Variables and Time-Varying Parameters

Statistics in Biosciences : Volume 6

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Statistics in Biosciences : Volume 2

Statistics in Biosciences : Volume 1

Joint Analysis of Survival Time and Longitudinal Categorical Outcomes

Content Provider	Springer Nature Link
Author	Choi, Jaeun Cai, Jianwen Zeng, Donglin Olshan, Andrew F.
Copyright Year	2013
Abstract	In biomedical or public health research, it is common for both survival time and longitudinal categorical outcomes to be collected for a subject, along with the subject’s characteristics or risk factors. Investigators are often interested in finding important variables for predicting both survival time and longitudinal outcomes which could be correlated within the same subject. Existing approaches for such joint analyses deal with continuous longitudinal outcomes. New statistical methods need to be developed for categorical longitudinal outcomes. We propose to simultaneously model the survival time with a stratified Cox proportional hazards model and the longitudinal categorical outcomes with a generalized linear mixed model. Random effects are introduced to account for the dependence between survival time and longitudinal outcomes due to unobserved factors. The Expectation–Maximization (EM) algorithm is used to derive the point estimates for the model parameters, and the observed information matrix is adopted to estimate their asymptotic variances. Asymptotic properties for our proposed maximum likelihood estimators are established using the theory of empirical processes. The method is demonstrated to perform well in finite samples via simulation studies. We illustrate our approach with data from the Carolina Head and Neck Cancer Study (CHANCE) and compare the results based on our simultaneous analysis and the separately conducted analyses using the generalized linear mixed model and the Cox proportional hazards model. Our proposed method identifies more predictors than by separate analyses.
Starting Page	19
Ending Page	47
Page Count	29
File Format	PDF
ISSN	18671764
Journal	Statistics in Biosciences
Volume Number	7
Issue Number	1
e-ISSN	18671772
Language	English
Publisher	Springer US
Publisher Date	2013-05-24
Publisher Institution	International Chinese Statistical Association
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	EM algorithm Generalized linear mixed model Maximum likelihood estimator Random effect Simultaneous modeling Stratified Cox proportional hazards model Statistics for Life Sciences, Medicine, Health Sciences Biostatistics Theoretical Ecology/Statistics
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Biochemistry, Genetics and Molecular Biology

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