Sample size and robust marginal methods for cluster-randomized trials with censored event times
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Date
2015-03-15
Authors
Cook, Richard J.
Zhong, Yujie
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley
Abstract
In cluster-randomized trials, intervention effects are often formulated by specifying marginal
models, fitting them under a working independence assumption, and using robust variance estimates
to address the association in the responses within clusters. We develop sample size criteria
within this framework, with analyses based on semiparametric Cox regression models fitted with
event times subject to right censoring. At the design stage, copula models are specified to enable
derivation of the asymptotic variance of estimators from a marginal Cox regression model
and to compute the number of clusters necessary to satisfy power requirements. Simulation studies
demonstrate the validity of the sample size formula in finite samples for a range of cluster
sizes, censoring rates and degrees of within-cluster association among event times. The power
and relative efficiency implications of copula misspecification is studied, as well as the effect of
within-cluster dependence in the censoring times. Sample size criteria and other design issues are
also addressed for the setting where the event status is only ascertained at periodic assessments
and times are interval censored.
Description
This is the peer reviewed version of the following article: Zhong Yujie, and Cook Richard J. (2015), Sample size and robust marginal methods for cluster-randomized trials with censored event times, Statist. Med., 34, pages 901–923. doi: 10.1002/sim.6395, which has been published in final form at http://dx.doi.org/10.1002/sim.6395. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Keywords
censored data, cluster-randomized trials, copula models, robust inference, sample size