Abstract:
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In biomedical or public health research, it is common for both survival time and longitudinal outcomes to be collected for a subject, along with the subject's characteristics or risk factors. Joint analysis of longitudinal outcomes and survival time is used to find important variables for predicting both longitudinal outcomes and survival time which are correlated within the same subject. Random effects are introduced to account for the dependence between survival time and longitudinal outcomes due to unobserved factors. In this work, we assume the underlying distribution for the random effect to be unknown. We propose to use a mixture of Gaussian distributions as an approximation in the estimation. Weights of the mixture components are estimated with model parameters using the Expectation-Maximization (EM) algorithm. The observed information matrix is adopted to estimate the asymptotic variances of the proposed estimators. The method is demonstrated to perform well in finite samples via simulation studies. We illustrate our approach with data from a cancer study.
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