Joint Modeling of Covariates and Censoring Process Assuming Non-Constant Dropout Hazard.

ABSTRACT: In this manuscript we propose a novel approach for the analysis of longitudinal data that have informative dropout. We jointly model the slopes of covariates of interest and the censoring process for which we assume a survival model with logistic non-constant dropout hazard in a likelihood function that is integrated over the random effects. Maximization of the marginal likelihood function results in acquiring maximum likelihood estimates for the population slopes and empirical Bayes estimates for the individual slopes that are predicted using Gaussian quadrature. Our simulation study results indicated that the performance of this model is superior in terms of accuracy and validity of the estimates compared to other models such as logistic non-constant hazard censoring model that does not

SUBMITTER: Jaffa MA 

PROVIDER: S-EPMC5010875 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature