A shared-parameter continuous-time hidden Markov and survival model for longitudinal data with informative dropout

Statistics in Medicine
Francesco Bartolucci, Alessio Farcomeni

Abstract

A shared-parameter approach for jointly modeling longitudinal and survival data is proposed. With respect to available approaches, it allows for time-varying random effects that affect both the longitudinal and the survival processes. The distribution of these random effects is modeled according to a continuous-time hidden Markov chain so that transitions may occur at any time point. For maximum likelihood estimation, we propose an algorithm based on a discretization of time until censoring in an arbitrary number of time windows. The observed information matrix is used to obtain standard errors. We illustrate the approach by simulation, even with respect to the effect of the number of time windows on the precision of the estimates, and by an application to data about patients suffering from mildly dilated cardiomyopathy.

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Citations

Sep 29, 2019·Multivariate Behavioral Research·Maria Francesca Marino, Marco Alfò
Jan 8, 2021·Multivariate Behavioral Research·Xiaoxiao ZhouXinyuan Song
Jul 6, 2020·Computer Methods and Programs in Biomedicine·Elif Dil, Duru Karasoy
May 20, 2021·Statistics in Medicine·Silvia Bianconcini, Silvia Cagnone
Jun 23, 2021·Multivariate Behavioral Research·Antonello MaruottiMatteo Rizzolli

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