Joint analysis of multivariate failure time data with latent variables

Abstract

We propose a joint modeling approach to investigate the observed and latent risk factors of the multivariate failure times of interest. The proposed model comprises two parts. The first part is a distribution-free confirmatory factor analysis model that characterizes the latent factors by correlated multiple observed variables. The second part is a multivariate additive hazards model that assesses the observed and latent risk factors of the failure times. A hybrid procedure that combines the borrow-strength estimation approach and the asymptotically distribution-free generalized least square method is developed to estimate the model parameters. The asymptotic properties of the proposed estimators are derived. Simulation studies demonstrate that the proposed method performs well for practical settings. An application to a study concerning the risk factors of multiple diabetic complications is provided.

Regression analysis of logistic model with latent variables

“…Pham et al 14 analyzed the effects of biologically structured latent factors on the drug's action mechanisms with the use of a joint modeling approach, wherein the latent factors (the biological pathways) in their conditional autoregressive model was characterized by several gene expressions through a CFA model. Pan et al [15][16][17] grouped clinical variables into latent medical traits of diabetic patients and evaluated the effect of risk factors on the hazards of diabetic complications.…”