Abstract
Bayesian MLIRT-based joint models for multivariate longitudinal and survival data with multiple features
Highlights
Data collected in many clinical studies can be grouped into three types: (i) longitudinal measurements of time-dependent biomarkers such as serum bilirubin (SB), serum albumin (SA), hepatomegaly (HM) and histologic stage (HS) data in primary biliary cirrhosis (PBC) clinical study [41]; (ii) event or censored times of interest; and (iii) information on some covariates that may affect the processes of both time-dependent longitudinal exposures and time-to-event
We examine the effect of multiple longitudinal exposures on the prognosis for patients with primary biliary cirrhosis (PBC) using data collected by the Mayo Clinic from 1974 to 1984 [18,41]
With more studies being conducted that repeatedly take measures over time in an effort to evaluate a patient’s health status for some events, an Multilevel item response theory (MLIRT)-based multivariate joint models (MJM) approach is powerful tool to fit these com- plicated longitudinal-survival models with a variety of data issues ranging from evaluating non-normality, correlated multivariate longitudinal measures and others, along with uncertainty about the distributional assumptions of model errors in clinical and observational studies
Summary
Data collected in many clinical studies can be grouped into three types: (i) longitudinal (repeated) measurements of time-dependent biomarkers such as serum bilirubin (SB), serum albumin (SA), hepatomegaly (HM) and histologic stage (HS) data in primary biliary cirrhosis (PBC) clinical study [41]; (ii) event or censored times of interest; and (iii) information on some covariates that may affect the processes of both time-dependent longitudinal exposures and time-to-event. In the second level structural multilevel model, the latent disease severity θij is regressed on covariates of interest, visit time, and random-effects.
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