A multistate joint model for interval-censored event-history data subject to within-unit clustering and informative missingness, with application to neurocysticercosis research

Abstract

We propose a multistate joint model to analyze interval-censored event-history data subject to within-unit clustering and nonignorable missing data. The model is motivated by a study of the neurocysticercosis (NC) cyst evolution at the cyst-level, taking into account the multiple cysts phases with intermittent missing data and loss to follow-up, as well as the intra-brain clustering of observations made on a predefined data collection schedule. Of particular interest in this study is the description of the process leading to cyst resolution, and whether this process varies by antiparasitic treatment. The model uses shared random effects to account for within-brain correlation and to explain the hidden heterogeneity governing the missing data mechanism. We developed a likelihood-based method using a Monte Carlo EM algorithm for the inference. The practical utility of the methods is illustrated using data from a randomized controlled trial on the effect of antiparasitic treatment with albendazole on NC cysts among patients from six hospitals in Ecuador. Simulation results demonstrate that the proposed methods perform well in the finite sample and misspecified models that ignore the data complexities could lead to substantial biases.