Sample size formulae for two-stage randomized trials with survival outcomes

Publication Abstract

Li, Zhiguo, and Susan A. Murphy. 2011. "Sample size formulae for two-stage randomized trials with survival outcomes." Biometrika, 98(3): 503-518.

Two-stage randomized trials are growing in importance in developing adaptive treatment strategies, i.e. treatment policies or dynamic treatment regimes. Usually, the first stage involves randomization to one of the several initial treatments. The second stage of treatment begins when an early nonresponse criterion or response criterion is met. In the second-stage, nonresponding subjects are re-randomized among second-stage treatments. Sample size calculations for planning these two-stage randomized trials with failure time outcomes are challenging because the variances of common test statistics depend in a complex manner on the joint distribution of time to the early nonresponse criterion or response criterion and the primary failure time outcome. We produce simple, albeit conservative, sample size formulae by using upper bounds on the variances. The resulting formulae only require the working assumptions needed to size a standard single-stage randomized trial and, in common settings, are only mildly conservative. These sample size formulae are based on either a weighted Kaplan-Meier estimator of survival probabilities at a fixed time-point or a weighted version of the log-rank test.

10.1093/biomet/asr019

PMCID: PMC3254237. (Pub Med Central)

Browse | Search | Next

PSC In The News

RSS Feed icon

Mehta makes it clear why young people are leading the rise of COVID cases in Michigan: Socializing

More News

Highlights

Frey's Social Science Data Analysis Network, SSDAN wins 2020 MERLOT Sociology Classics Award

Doing COVID-19 research? These data tools can help!

More Highlights


Connect with PSC follow PSC on Twitter Like PSC on Facebook