A sample size formula for the supremum log-rank statistic.
An advantage of the supremum log-rank over the standard log-rank statistic is an increased sensitivity to a wider variety of stochastic ordering alternatives. In this article, we develop a formula for sample size computation for studies utilizing the supremum log-rank statistic. The idea is to base power on the proportional hazards alternative, so that the supremum log rank will have the same power as the standard log rank in the setting where the standard log rank is optimal. This results in a slight increase in sample size over that required for the standard log rank. For example, a 5.733% increase occurs for a two-sided test having type I error 0.05 and power 0.80. This slight increase in sample size is offset by the significant gains in power the supremum log-rank test achieves for a wide range of nonproportional hazards alternatives. A small simulation study is used for illustration. These results should facilitate the wider use of the supremum log-rank statistic in clinical trials.
Eng
Kevin Hasegawa
KH
Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53792, USA.
Kosorok
Michael R
MR
D001699:Biometry; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D016016:Proportional Hazards Models; D018401:Sample Size; D016019:Survival Analysis