Research article

Testing for differences in distribution tails to test for differences in 'maximum' lifespan

Guimin Gao¹ , Wen Wan⁴ , Sijian Zhang⁵ , David T Redden^1,2,3 and David B Allison^1,2,3

¹  Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA

²  Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, Alabama, USA

³  Clinical Nutrition Research Center, University of Alabama at Birmingham, Birmingham, Alabama, USA

⁴  Biostatistics and Bioinformatics Unit, Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, Alabama, USA

⁵  Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA

author email corresponding author email

BMC Medical Research Methodology 2008, 8:49doi:10.1186/1471-2288-8-49

Published:	25 July 2008

Abstract

Background

Investigators are actively testing interventions intended to increase lifespan and wish to test whether the interventions increase maximum lifespan. Based on the fact that one cannot be assured of observing population maximum lifespans in finite samples, in previous work, we constructed and validated several tests of difference in the upper parts of lifespan distributions between a treatment group and a control group by testing whether the probabilities that observations are above some threshold defining 'old' or being in the tail of the survival distribution are equal in the two groups. However, a limitation of these tests is that they do not consider how much above the threshold any particular observation is.

Methods

In this article we propose new methods which improve upon our previous tests by considering not only whether an observation is above some threshold, but also the magnitudes by which observations exceed the threshold.

Results

Simulations show that the new methods control type I error rates quite well and that the power of the new methods is usually higher than that of the tests we previously proposed. In illustrative analyses of two real datasets involving rodents, when setting the threshold equal to 110 (100) weeks for the first (second) datasets, the new methods detected differences in 'maximum lifespan' between groups at nominal alpha levels of 0.01 (0.05) for the first (second) datasets and provided more significant results than competitor tests.

Conclusion

The new methods not only have good performance in controlling the type I error rates but also improve the power compared with the tests we previously proposed.