A multistate joint model for interval‐censored event‐history data subject to within‐unit clustering and informative missingness, with application to neurocysticercosis research
| Autor: | Elizabeth A. Kelvin, Arturo Carpio, Hongbin Zhang, W. Allen Hauser |
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| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
Schedule Epidemiology Computer science Inference Sample (statistics) Interval (mathematics) Neurocysticercosis computer.software_genre 01 natural sciences 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Cluster Analysis Humans 030212 general & internal medicine 0101 mathematics Cluster analysis Likelihood Functions Models Statistical Data collection Random effects model Missing data Data mining Monte Carlo Method computer |
| Zdroj: | Statistics in Medicine. 39:3195-3206 |
| ISSN: | 1097-0258 0277-6715 |
| DOI: | 10.1002/sim.8663 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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