Multilevel model with random effects for clustered survival data with multiple failure outcomes

Tawiah, R; Yau, KKW; McLachlan, GJ; Chambers, SK; Ng, SK

Multilevel model with random effects for clustered survival data with multiple failure outcomes

Tawiah, R Yau, KKW McLachlan, GJ Chambers, SK

Ng, SK

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Publication Type:: Journal Article
Citation:: Statistics in Medicine, 2019, 38 (6), pp. 1036 - 1055
Issue Date:: 2019-03-15

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Field	Value	Language
dc.contributor.author	Tawiah, R	en_US
dc.contributor.author	Yau, KKW	en_US
dc.contributor.author	McLachlan, GJ	en_US
dc.contributor.author	Chambers, SK https://orcid.org/0000-0003-2369-6111	en_US
dc.contributor.author	Ng, SK	en_US
dc.date.available	2018-10-27	en_US
dc.date.issued	2019-03-15	en_US
dc.identifier.citation	Statistics in Medicine, 2019, 38 (6), pp. 1036 - 1055	en_US
dc.identifier.issn	0277-6715	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135637
dc.description.abstract	© 2018 John Wiley & Sons, Ltd. We present a multilevel frailty model for handling serial dependence and simultaneous heterogeneity in survival data with a multilevel structure attributed to clustering of subjects and the presence of multiple failure outcomes. One commonly observes such data, for example, in multi-institutional, randomized placebo-controlled trials in which patients suffer repeated episodes (eg, recurrent migraines) of the disease outcome being measured. The model extends the proportional hazards model by incorporating a random covariate and unobservable random institution effect to respectively account for treatment-by-institution interaction and institutional variation in the baseline risk. Moreover, a random effect term with correlation structure driven by a first-order autoregressive process is attached to the model to facilitate estimation of between patient heterogeneity and serial dependence. By means of the generalized linear mixed model methodology, the random effects distribution is assumed normal and the residual maximum likelihood and the maximum likelihood methods are extended for estimation of model parameters. Simulation studies are carried out to evaluate the performance of the residual maximum likelihood and the maximum likelihood estimators and to assess the impact of misspecifying random effects distribution on the proposed inference. We demonstrate the practical feasibility of the modeling methodology by analyzing real data from a double-blind randomized multi-institutional clinical trial, designed to examine the effect of rhDNase on the occurrence of respiratory exacerbations among patients with cystic fibrosis.	en_US
dc.relation.ispartof	Statistics in Medicine	en_US
dc.relation.isbasedon	10.1002/sim.8041	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Multilevel model with random effects for clustered survival data with multiple failure outcomes	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	38	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1117 Public Health and Health Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	38	en_US

Abstract:

© 2018 John Wiley & Sons, Ltd. We present a multilevel frailty model for handling serial dependence and simultaneous heterogeneity in survival data with a multilevel structure attributed to clustering of subjects and the presence of multiple failure outcomes. One commonly observes such data, for example, in multi-institutional, randomized placebo-controlled trials in which patients suffer repeated episodes (eg, recurrent migraines) of the disease outcome being measured. The model extends the proportional hazards model by incorporating a random covariate and unobservable random institution effect to respectively account for treatment-by-institution interaction and institutional variation in the baseline risk. Moreover, a random effect term with correlation structure driven by a first-order autoregressive process is attached to the model to facilitate estimation of between patient heterogeneity and serial dependence. By means of the generalized linear mixed model methodology, the random effects distribution is assumed normal and the residual maximum likelihood and the maximum likelihood methods are extended for estimation of model parameters. Simulation studies are carried out to evaluate the performance of the residual maximum likelihood and the maximum likelihood estimators and to assess the impact of misspecifying random effects distribution on the proposed inference. We demonstrate the practical feasibility of the modeling methodology by analyzing real data from a double-blind randomized multi-institutional clinical trial, designed to examine the effect of rhDNase on the occurrence of respiratory exacerbations among patients with cystic fibrosis.

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http://hdl.handle.net/10453/135637