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A multistate joint model for interval-censored event-history data subject to within-unit clustering and informative missingness, with application to neurocysticercosis research.
Stat Med. 2020 Jun 25 [Online ahead of print]SM

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.

Authors+Show Affiliations

Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, Institute for Implementation Science in Population Health, City University of New York, New York, New York, United States.Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, Institute for Implementation Science in Population Health, City University of New York, New York, New York, United States.School of Medicine, University of Cuenca, Cuenca, Ecuador.Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, United States.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32584425

Citation

Zhang, Hongbin, et al. "A Multistate Joint Model for Interval-censored Event-history Data Subject to Within-unit Clustering and Informative Missingness, With Application to Neurocysticercosis Research." Statistics in Medicine, 2020.
Zhang H, Kelvin EA, Carpio A, et al. A multistate joint model for interval-censored event-history data subject to within-unit clustering and informative missingness, with application to neurocysticercosis research. Stat Med. 2020.
Zhang, H., Kelvin, E. A., Carpio, A., & Allen Hauser, W. (2020). A multistate joint model for interval-censored event-history data subject to within-unit clustering and informative missingness, with application to neurocysticercosis research. Statistics in Medicine. https://doi.org/10.1002/sim.8663
Zhang H, et al. A Multistate Joint Model for Interval-censored Event-history Data Subject to Within-unit Clustering and Informative Missingness, With Application to Neurocysticercosis Research. Stat Med. 2020 Jun 25; PubMed PMID: 32584425.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A multistate joint model for interval-censored event-history data subject to within-unit clustering and informative missingness, with application to neurocysticercosis research. AU - Zhang,Hongbin, AU - Kelvin,Elizabeth A, AU - Carpio,Arturo, AU - Allen Hauser,W, Y1 - 2020/06/25/ PY - 2019/07/16/received PY - 2020/01/15/revised PY - 2020/05/19/accepted PY - 2020/6/26/entrez KW - frailty survival model KW - interval-censoring KW - multistate joint model KW - neurocysticercosis KW - nonignorable missingness JF - Statistics in medicine JO - Stat Med N2 - 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. SN - 1097-0258 UR - https://www.unboundmedicine.com/medline/citation/32584425/A_multistate_joint_model_for_interval-censored_event-history_data_subject_to_within-unit_clustering_and_informative_missingness,_with_application_to_neurocysticercosis_research L2 - https://doi.org/10.1002/sim.8663 DB - PRIME DP - Unbound Medicine ER -
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