Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates.We propose a joint model for longitudinal and survival data with time-varying covariates subject to detection limits and intermittent missingness at random.  The model is motivated by data from the Multicenter AIDS Cohort Study (MACS), in which HIV+ subjects have viral load and CD4 cell count measured at repeated visits along with survival data.  We model the longitudinal component using a normal linear mixed model, modeling the trajectory of CD4 cell count by regressing on viral load, and other covariates.  The viral load data are subject to both left censoring because of detection limits (17%) and intermittent missingness (27%).  The survival component of the joint model is a Cox model with time-dependent covariates for death because of AIDS.  The longitudinal and survival models are linked using the trajectory function of the linear mixed model.  A Bayesian analysis is conducted on the MACS data using the proposed joint model.  The proposed method is shown to improve the precision of estimates when compared with alternative methods.
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Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates.We propose a joint model for longitudinal and survival data with time-varying covariates subject to detection limits and intermittent missingness at random.  The model is motivated by data from the Multicenter AIDS Cohort Study (MACS), in which HIV+ subjects have viral load and CD4 cell count measured at repeated visits along with survival data.  We model the longitudinal component using a normal linear mixed model, modeling the trajectory of CD4 cell count by regressing on viral load, and other covariates.  The viral load data are subject to both left censoring because of detection limits (17%) and intermittent missingness (27%).  The survival component of the joint model is a Cox model with time-dependent covariates for death because of AIDS.  The longitudinal and survival models are linked using the trajectory function of the linear mixed model.  A Bayesian analysis is conducted on the MACS data using the proposed joint model.  The proposed method is shown to improve the precision of estimates when compared with alternative methods.
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