Bayesian MLIRT-based joint models for multivariate longitudinal and survival data with multiple features

Abstract

Methodological development and applications of joint models for longitudinal and survival data have mostly coupled a single longitudinal outcome-based mixed-effects model with normal distribution and Cox proportional hazards model. In practice, however, (i) normality of model error in longitudinal sub-models is a routine assumption, but it may be unrealistically violating data features of subject variations. (ii) The data collected are often featured by multivariate longitudinal outcomes which are significantly correlated, ignoring their correlation may lead to biased estimation. Additionally, a parametric specification may be inflexible to capture the complicated longitudinal pattern of biomarkers. (iii) It is of importance to investigate how multivariate longitudinal outcomes are associated with an event time of interest. Multilevel item response theory (MLIRT) models have been increasingly used to analyze the multivariate longitudinal data of mixed types (e.g., continuous and categorical) in clinical studies. In this article, we develop a multivariate joint model that consists of an extended MLIRT model for the mixed types of multivariate longitudinal data and a Cox proportional hazards model, linked through random-effects. The proposed models and method are applied to analyze longitudinalsurvival data arising from a primary biliary cirrhosis study. Simulation studies are conducted to evaluate the performance of the proposed models and method.

Semiparametric multivariate joint model for skewed‐longitudinal and survival data: A Bayesian approach

“…[21][22][23] Advantages of the MLIRT models include better reflection of multilevel data structure, simultaneous estimation of measurement-specific parameters and covariate effects, and accurate inference about high-level measures. [24][25][26] To gain valid inference from the MLIRT models, marginal maximum likelihood methods, 23 and Bayesian methods 19,24,[27][28][29][30][31][32] have been widely adopted. To the best of our knowledge, no studies have done to explore semiparametric multivariate joint models (SMJM) with the ST distribution, coupled with an MLIRT submodel for multivariate longitudinal variables of mixed types with various data features and a Cox proportional hazards submodel for survival endpoint, linked through random-effects.…”