H‐likelihood approach for joint modeling of longitudinal outcomes and time‐to‐event data

Abstract

In longitudinal studies, a subject may have different types of outcomes that could be correlated. For example, a response variable of interest would be measured repeatedly over time on the same subject and at the same time, an event time representing a single event or competing-risks event is also observed. In this paper, we propose a joint modeling framework that accounts for the inherent association between such multiple outcomes via frailties (unobserved random effects). Among outcomes, at least one outcome is an event time that has a type of a single event or competing-risks event. For inference we use the hierarchical likelihood (h-likelihood) that provides an unified efficient fitting procedure for the joint models. Numerical studies are provided to show the performance of the proposed method and two data examples are shown.

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“…The aim of inferential statistics is to derive from the results the findings of the analysis accompanying the population. HGLM is a statistical modeling that appears to offer consistency in modeling and broad application, including the use of penalized-likelihood scenarios [35], factor analysis ordinal data that can be used for structural equation modeling [36], modeling longitudinal outcomes time to event [37].…”

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“…This package allows various models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. See also further HGLM applications for machine learning [4], schizophrenic behavior data [8], variable selection methods [9], non-Gaussian factor [10], factor analysis for ordinal data [11], survival analysis [12], longitudinal outcomes and time-to-event data [13], and recent advanced topics [14][15][16][17].…”