A MCEM based multistate model of interval-censored and correlated panel data for neurocysticercosis study

Hongbin Zhang; Elizabeth Kelvin; Arturo Carpio; Allen Hauser

doi:10.11159/icsta20.105

A MCEM based multistate model of interval-censored and correlated panel data for neurocysticercosis study

Hongbin Zhang
, Elizabeth Kelvin
, Arturo Carpio
, Allen Hauser

Producción científica: Conference contribution › revisión exhaustiva

Resumen

We propose a multistate model to analyse interval-censored event-history data subject to within-unit clustering. The model is motivated by a study of the neurocysticercosis evolution at cyst-level, considering the multiple cysts phases within the brain at pre-scheduled imaging time points. Of interest is the study of the intra-brain distribution of the process leading to cyst resolution, and whether this distribution varies with the anthelmintic treatment. We develop a likelihood-based method using Monto Carlo EM algorithm for the inference. The practical utility of the methods is illustrated using data from a longitudinal study on albendazole's therapeutic effect among patients from six hospitals in Ecuador.

Idioma original	English
Título de la publicación alojada	2nd International Conference on Statistics
Subtítulo de la publicación alojada	Theory and Applications, ICSTA 2020
Editores	Gangaram S. Ladde, Noelle Samia
Editorial	Avestia Publishing
ISBN (versión impresa)	9781927877685
DOI	https://doi.org/10.11159/icsta20.105
Estado	Published - 2020
Evento	2nd International Conference on Statistics: Theory and Applications, ICSTA 2020 - Virtual, Online Duración: ago 19 2020 → ago 21 2020

Serie de la publicación

Nombre	Proceedings of the International Conference on Statistics
ISSN (versión digital)	2562-7767

Conference

Conference	2nd International Conference on Statistics: Theory and Applications, ICSTA 2020
Ciudad	Virtual, Online
Período	8/19/20 → 8/21/20

Nota bibliográfica

Publisher Copyright:
© 2020, Avestia Publishing. All rights reserved.

Financiación

This original RCT was supported by the National Institute of Neurological Disorders and Stroke at the National Institutes of Health [R01-NS39403]. The analyses presented here were supported by the City University of New York [PSC-CUNY ENHC 47]. This work was also partially supported by the CUNY (City University of New York) High Performance Computing Centre, College of Staten Island, funded in part by the City and State of New York, CUNY Research Foundation, and National Science Foundation Grants CNS-0958379, CNS-0855217 and ACI-1126113.

Financiadores	Número del financiador
College of Staten Island, City University of New York
City University of New York) High Performance Computing Centre
Institute of Neurological Disorders and Stroke National Advisory Neurological Disorders and Stroke Council
City and State of New York, City University of New York Research Foundation	PSC-CUNY ENHC 47
National Science Foundation Arctic Social Science Program	CNS-0958379, ACI-1126113, CNS-0855217, 0958379
National Institutes of Health (NIH)	R01-NS39403

ASJC Scopus subject areas

Applied Mathematics
Computational Mathematics
Statistics and Probability
Theoretical Computer Science

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10.11159/icsta20.105

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Zhang, H., Kelvin, E., Carpio, A., & Hauser, A. (2020). A MCEM based multistate model of interval-censored and correlated panel data for neurocysticercosis study. En G. S. Ladde, & N. Samia (Eds.), 2nd International Conference on Statistics: Theory and Applications, ICSTA 2020 Artículo 105 (Proceedings of the International Conference on Statistics). Avestia Publishing. https://doi.org/10.11159/icsta20.105

Zhang, Hongbin ; Kelvin, Elizabeth ; Carpio, Arturo et al. / A MCEM based multistate model of interval-censored and correlated panel data for neurocysticercosis study. 2nd International Conference on Statistics: Theory and Applications, ICSTA 2020. editor / Gangaram S. Ladde ; Noelle Samia. Avestia Publishing, 2020. (Proceedings of the International Conference on Statistics).

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title = "A MCEM based multistate model of interval-censored and correlated panel data for neurocysticercosis study",

abstract = "We propose a multistate model to analyse interval-censored event-history data subject to within-unit clustering. The model is motivated by a study of the neurocysticercosis evolution at cyst-level, considering the multiple cysts phases within the brain at pre-scheduled imaging time points. Of interest is the study of the intra-brain distribution of the process leading to cyst resolution, and whether this distribution varies with the anthelmintic treatment. We develop a likelihood-based method using Monto Carlo EM algorithm for the inference. The practical utility of the methods is illustrated using data from a longitudinal study on albendazole's therapeutic effect among patients from six hospitals in Ecuador.",

keywords = "MCEM algorithm, correlated disease progressions, frailty survival model, interval-censoring, multistate model, neurocysticercosis",

author = "Hongbin Zhang and Elizabeth Kelvin and Arturo Carpio and Allen Hauser",

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Zhang, H, Kelvin, E, Carpio, A & Hauser, A 2020, A MCEM based multistate model of interval-censored and correlated panel data for neurocysticercosis study. En GS Ladde & N Samia (eds.), 2nd International Conference on Statistics: Theory and Applications, ICSTA 2020., 105, Proceedings of the International Conference on Statistics, Avestia Publishing, 2nd International Conference on Statistics: Theory and Applications, ICSTA 2020, Virtual, Online, 8/19/20. https://doi.org/10.11159/icsta20.105

A MCEM based multistate model of interval-censored and correlated panel data for neurocysticercosis study. / Zhang, Hongbin; Kelvin, Elizabeth; Carpio, Arturo et al.
2nd International Conference on Statistics: Theory and Applications, ICSTA 2020. ed. / Gangaram S. Ladde; Noelle Samia. Avestia Publishing, 2020. 105 (Proceedings of the International Conference on Statistics).

Producción científica: Conference contribution › revisión exhaustiva

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AU - Carpio, Arturo

AU - Hauser, Allen

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N2 - We propose a multistate model to analyse interval-censored event-history data subject to within-unit clustering. The model is motivated by a study of the neurocysticercosis evolution at cyst-level, considering the multiple cysts phases within the brain at pre-scheduled imaging time points. Of interest is the study of the intra-brain distribution of the process leading to cyst resolution, and whether this distribution varies with the anthelmintic treatment. We develop a likelihood-based method using Monto Carlo EM algorithm for the inference. The practical utility of the methods is illustrated using data from a longitudinal study on albendazole's therapeutic effect among patients from six hospitals in Ecuador.

AB - We propose a multistate model to analyse interval-censored event-history data subject to within-unit clustering. The model is motivated by a study of the neurocysticercosis evolution at cyst-level, considering the multiple cysts phases within the brain at pre-scheduled imaging time points. Of interest is the study of the intra-brain distribution of the process leading to cyst resolution, and whether this distribution varies with the anthelmintic treatment. We develop a likelihood-based method using Monto Carlo EM algorithm for the inference. The practical utility of the methods is illustrated using data from a longitudinal study on albendazole's therapeutic effect among patients from six hospitals in Ecuador.

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KW - correlated disease progressions

KW - frailty survival model

KW - interval-censoring

KW - multistate model

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Zhang H, Kelvin E, Carpio A, Hauser A. A MCEM based multistate model of interval-censored and correlated panel data for neurocysticercosis study. En Ladde GS, Samia N, editores, 2nd International Conference on Statistics: Theory and Applications, ICSTA 2020. Avestia Publishing. 2020. 105. (Proceedings of the International Conference on Statistics). doi: 10.11159/icsta20.105

A MCEM based multistate model of interval-censored and correlated panel data for neurocysticercosis study

Resumen

Serie de la publicación

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Nota bibliográfica

Financiación

ASJC Scopus subject areas

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