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Article: Estimation of Intervention Effects Using Recurrent Event Time Data in the Presence of Event Dependence and a Cured Fraction

TitleEstimation of Intervention Effects Using Recurrent Event Time Data in the Presence of Event Dependence and a Cured Fraction
Authors
Issue Date2014
PublisherJohn Wiley & Sons. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/
Citation
Statistics in Medicine, 2014, v. 33n. 13, p. 2263-2274 How to Cite?
AbstractRecurrent event data with a fraction of subjects having zero event are often seen in randomized clinical trials. Those with zero event may belong to a cured (or non-susceptible) fraction. Event dependence refers to the situation that a person’s past event history affects his future event occurrences. In the presence of event dependence, an intervention may have an impact on the event rate in the non-cured through two pathways— a primary effect directly on the outcome event and a secondary effect mediated through event dependence. The primary effect combined with the secondary effect is the total effect. We propose a frailty mixture model and a two-step estimation procedure for the estimation of the effect of an intervention on the probability of cure and the total effect on event rate in the non-cured. A summary measure of intervention effects is derived. The performance of the proposed model is evaluated by simulation. Data on respiratory exacerbations from a randomized, placebo-controlled trial are re-analyzed for illustration.
Persistent Identifierhttp://hdl.handle.net/10722/203416
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXu, Yen_US
dc.contributor.authorLam, KFen_US
dc.contributor.authorCheung, YBen_US
dc.date.accessioned2014-09-19T15:10:24Z-
dc.date.available2014-09-19T15:10:24Z-
dc.date.issued2014en_US
dc.identifier.citationStatistics in Medicine, 2014, v. 33n. 13, p. 2263-2274en_US
dc.identifier.urihttp://hdl.handle.net/10722/203416-
dc.description.abstractRecurrent event data with a fraction of subjects having zero event are often seen in randomized clinical trials. Those with zero event may belong to a cured (or non-susceptible) fraction. Event dependence refers to the situation that a person’s past event history affects his future event occurrences. In the presence of event dependence, an intervention may have an impact on the event rate in the non-cured through two pathways— a primary effect directly on the outcome event and a secondary effect mediated through event dependence. The primary effect combined with the secondary effect is the total effect. We propose a frailty mixture model and a two-step estimation procedure for the estimation of the effect of an intervention on the probability of cure and the total effect on event rate in the non-cured. A summary measure of intervention effects is derived. The performance of the proposed model is evaluated by simulation. Data on respiratory exacerbations from a randomized, placebo-controlled trial are re-analyzed for illustration.en_US
dc.languageengen_US
dc.publisherJohn Wiley & Sons. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/en_US
dc.relation.ispartofStatistics in Medicineen_US
dc.rightsStatistics in Medicine. Copyright © John Wiley & Sons.en_US
dc.titleEstimation of Intervention Effects Using Recurrent Event Time Data in the Presence of Event Dependence and a Cured Fractionen_US
dc.typeArticleen_US
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hken_US
dc.identifier.authorityLam, KF=rp00718en_US
dc.identifier.doi10.1002/sim.6093en_US
dc.identifier.pmid24449504-
dc.identifier.hkuros235534en_US
dc.identifier.volume33en_US
dc.identifier.spage2263en_US
dc.identifier.epage2274en_US
dc.identifier.isiWOS:000335772800008-

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