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

Biostatistics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2nd International Conference on Statistics
Subtitle of host publication	Theory and Applications, ICSTA 2020
Editors	Gangaram S. Ladde, Noelle Samia
Publisher	Avestia Publishing
ISBN (Print)	9781927877685
DOIs	https://doi.org/10.11159/icsta20.105
State	Published - 2020
Event	2nd International Conference on Statistics: Theory and Applications, ICSTA 2020 - Virtual, Online Duration: Aug 19 2020 → Aug 21 2020

Publication series

Name	Proceedings of the International Conference on Statistics
ISSN (Electronic)	2562-7767

Conference

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

Bibliographical note

Funding Information:
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.

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

Keywords

MCEM algorithm
correlated disease progressions
frailty survival model
interval-censoring
multistate model
neurocysticercosis

ASJC Scopus subject areas

Applied Mathematics
Computational Mathematics
Statistics and Probability
Theoretical Computer Science

Access to Document

10.11159/icsta20.105

Cite this

Zhang, H., Kelvin, E., Carpio, A., & Hauser, A. (2020). A MCEM based multistate model of interval-censored and correlated panel data for neurocysticercosis study. In G. S. Ladde, & N. Samia (Eds.), 2nd International Conference on Statistics: Theory and Applications, ICSTA 2020 Article 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",

note = "Funding Information: 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. Publisher Copyright: {\textcopyright} 2020, Avestia Publishing. All rights reserved.; 2nd International Conference on Statistics: Theory and Applications, ICSTA 2020 ; Conference date: 19-08-2020 Through 21-08-2020",

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doi = "10.11159/icsta20.105",

language = "English",

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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. in 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N1 - Funding Information: 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. Publisher Copyright: © 2020, Avestia Publishing. All rights reserved.

PY - 2020

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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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Zhang H, Kelvin E, Carpio A, Hauser A. A MCEM based multistate model of interval-censored and correlated panel data for neurocysticercosis study. In Ladde GS, Samia N, editors, 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

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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