Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Eisenberg says college athletes much less likely than other students to seek help with mental health conditions

Mitchell finds children who lose fathers suffer at cellular level

Seefeldt says hard work alone won't allow poor to reach middle-class status in America

More News

Highlights

Neal Krause wins GSA's Robert Kleemeier Award

U-M awarded $58 million to develop ideas for preventing and treating health problems

Bailey, Eisenberg , and Fomby promoted at PSC

Former PSC trainee Eric Chyn wins PAA's Dorothy S. Thomas Award for best paper

More Highlights

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.

DOI:10.1093/biomet/asr019 (Full Text)

PMCID: PMC3254237. (Pub Med Central)

Browse | Search : All Pubs | Next