Bouliotis , Georgios and Billingham, Lucinda J. (2011) Crossing survival curves : alternatives to the log-rank test. In: MRC Clinical Trials Methodology Conference 2011, Bristol, 4-5 Oct 2011. Published in: Trials, 12 doi:10.1186/1745-6215-12-S1-A137 ISSN 1745-6215.

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Abstract

Background:
It is not uncommon for clinical trials to present results on survival time as Kaplan-Meier survival curves that cross, indicating non-proportional hazards. A recent example was given in a pivotal trial in advanced non-small cell lung cancer (The ‘IPASS study’ [1]). Trials such as these present a hazard ratio and log-rank test for treatment comparison as this is their planned primary analysis. However, the validity of such analysis is questionable and has received published criticism. This paper reviews the use of the log-rank test with crossing curves and considers alternatives that have been proposed.

Methods:
The review of the alternative approaches includes weighted log-rank tests (Wilcoxon, Tarone-Ware, Peto-Prentice and Fleming-Harrington), supremum versions of the log-rank test (modified Kolmogorov-Smirnov and Renyi-type tests) which are based on the maximum difference between estimates of two survivor functions and modified log-rank tests (Lin and Wang test using squared differences at each time point, and Levene-type test focusing on variance differences). In addition, methods based on splitting the analysis at the crossing point have also been proposed. Methods are compared and evaluated using both real and simulated datasets using Weibull and Weibull-Cox distributions representing realistic situations.

Results:
Crossing survival curves is generally a result of the survival times having greater variance in one treatment group than another. The performance of the log-rank test and alternatives depend on the type of crossing (early, mid or late) but in general the probability of a Type II error is increased for log-rank and weighted log-rank tests but performance is improved with the alternatives. The choice of time-point for the split-analysis is problematic. Standard software such as sts test (Stata), proc lifetest (SAS) and survfit (R) and routines-on-demand support some but not all the tests considered.

Conclusions:
There is a need in the clinical community to clarify methods that are appropriate when survival curves cross. Statistical analysis plans for clinical trials with survival as primary outcome measure should specify an analysis dependent on the proportionality of hazard rates and explicitly consider non-proportionality issues, powering the analyses based on log-rank alternatives. Modelling the survival data may be more appropriate than simple univariate hypothesis tests when hazards are not proportional. Finally, there are some feasibility issues regarding software for such analysis that remain to be tackled.

Item Type:	Conference Item (Paper)
Divisions:	Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Clinical Trials Unit Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
Journal or Publication Title:	Trials
Publisher:	Biomed Central
ISSN:	1745-6215
Official Date:	13 December 2011
Dates:	Date Event 13 December 2011 Published 13 December 2011 Accepted
Volume:	12
Article Number:	A137
DOI:	10.1186/1745-6215-12-S1-A137
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Conference Paper Type:	Paper
Title of Event:	MRC Clinical Trials Methodology Conference 2011
Type of Event:	Conference
Location of Event:	Bristol
Date(s) of Event:	4-5 Oct 2011
Open Access Version:	Publisher

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