Joint Analysis of Survival Time and Longitudinal Categorical Outcomes

• Published in: Statistics in biosciences Vol. 7; no. 1; pp. 19 - 47
• Main Authors: Choi, Jaeun, Cai, Jianwen, Zeng, Donglin, Olshan, Andrew F.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2015
• Subjects: Biostatistics; Health Sciences; Mathematics and Statistics; Medicine; Statistics; Statistics for Life Sciences; Theoretical Ecology/Statistics; Maximum likelihood estimator; Simultaneous modeling; Stratified Cox proportional hazards model; Random effect; EM algorithm; Generalized linear mixed model
• ISSN: 1867-1764; 1867-1772
• DOI: 10.1007/s12561-013-9091-z

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.