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Estimation of intervention effects using recurrent event time data in the presence of event dependence and a cured fraction
Author(s) -
Xu Ying,
Lam K. F.,
Cheung Yin Bun
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6093
Subject(s) - event (particle physics) , fraction (chemistry) , event study , estimation , statistics , econometrics , randomized controlled trial , intervention (counseling) , mathematics , computer science , medicine , history , economics , physics , context (archaeology) , chemistry , management , organic chemistry , archaeology , quantum mechanics , psychiatry
Recurrent 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. Copyright © 2014 John Wiley & Sons, Ltd.