A nonparametric test for equality of survival medians using right-censored prevalent cohort survival data

The median is a robust summary commonly used for comparison between populations. The existing literature falls short in testing for equality of survival medians when the collected data do not form representative samples from their respective target populations and are subject to right censoring. Such data commonly occur in prevalent cohort studies with follow-up. We consider a particular case where the disease under study is stable, that is, the incidence rate of the disease is stable. It is known that survival data collected on diseased cases, when the disease under study is stable, form a length-biased sample from the target population. We fill the gap for the particular case of length-biased right-censored survival data by proposing a large-sample test using the nonparametric maximum likelihood estimator of the survivor function in the target population. The small sample performance of the proposed test statistic is studied via simulation. We apply the proposed method to test for differences in survival medians of Alzheimer's disease and dementia groups using the survival data collected as part of the Canadian Study of Health and Aging.

Automated summary

The paper focuses on testing for differences in survival medians when collected data are not representative samples and subject to right censoring. The authors propose a large-sample test using the nonparametric maximum likelihood estimator of the survivor function in the target population and examine its small sample performance through simulation. The method is applied to test for differences in survival medians of Alzheimer's disease and dementia groups.