Sample size calculation for studies with grouped survival data.
Abstract
Grouped survival data arise often in studies where the disease status is assessed
at regular visits to clinic. The time to the event of interest can only be determined
to be between two adjacent visits or is right censored at one visit. In data analysis,
replacing the survival time with the endpoint or midpoint of the grouping interval
leads to biased estimators of the effect size in group comparisons. Prentice and Gloeckler
developed a maximum likelihood estimator for the proportional hazards model with grouped
survival data and the method has been widely applied. Previous work on sample size
calculation for designing studies with grouped data is based on either the exponential
distribution assumption or the approximation of variance under the alternative with
variance under the null. Motivated by studies in HIV trials, cancer trials and in
vitro experiments to study drug toxicity, we develop a sample size formula for studies
with grouped survival endpoints that use the method of Prentice and Gloeckler for
comparing two arms under the proportional hazards assumption. We do not impose any
distributional assumptions, nor do we use any approximation of variance of the test
statistic. The sample size formula only requires estimates of the hazard ratio and
survival probabilities of the event time of interest and the censoring time at the
endpoints of the grouping intervals for one of the two arms. The formula is shown
to perform well in a simulation study and its application is illustrated in the three
motivating examples.
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Journal articlePermalink
https://hdl.handle.net/10161/17196Published Version (Please cite this version)
10.1002/sim.7847Publication Info
Li, Zhiguo; Wang, Xiaofei; Wu, Yuan; & Owzar, Kouros (2018). Sample size calculation for studies with grouped survival data. Statistics in medicine. 10.1002/sim.7847. Retrieved from https://hdl.handle.net/10161/17196.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Zhiguo Li
Associate Professor of Biostatistics & Bioinformatics
survival analysis, dynamic treatment regimes, clinical trials
Kouros Owzar
Professor of Biostatistics & Bioinformatics
cancer pharmacogenomicsdrug induced neuropathy, neutropenia and hypertensionstatistical
genetics statistical methods for high-dimensional data copulas survival analysis statistical
computing
Xiaofei Wang
Professor of Biostatistics & Bioinformatics
Design and Analysis of Clinical TrialsMethods for Diagnostic and Predictive Medicine
Survival AnalysisCausal InferenceAnalysis of Data from Multiple Sources
Yuan Wu
Associate Professor in Biostatistics & Bioinformatics
Survival analysis, Sequential clinical trial design, Machine learning, Causal inference,
Non/Semi-parametric method, Statistical computing
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