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Stat Med. 2014 Jun 15;33(13):2263-74. doi: 10.1002/sim.6093. Epub 2014 Jan 22.

Estimation of intervention effects using recurrent event time data in the presence of event dependence and a cured fraction.

Author information

1
Centre for Quantitative Medicine, Office of Clinical Sciences, Duke-NUS Graduate Medical School, Singapore; Scientific Development Division, Singapore Clinical Research Institute, Singapore.

Abstract

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.

KEYWORDS:

event dependence; frailty mixture model; intervention effects; recurrent events

PMID:
24449504
DOI:
10.1002/sim.6093
[Indexed for MEDLINE]

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