A comparative study of two-sample tests for interval-censored data

Abstract

Interval-censored data are ubiquitous in clinical studies where actual time-to-event is difficult to measure. A number of nonparametric tests have been proposed to conduct a two-sample test using interval-censored data, and these tests can be used for assessing and comparing treatment effects over the control group. Alternatively, as commonly perceived, parametric tests can also be used assuming data are generated from a parametric family of distributions. To provide some guidance on choosing an appropriate method, in this paper, the performance of parametric tests and a series of nonparametric tests are compared through extensive simulation studies that cover a wide range of scenarios with varying sample sizes, varying censoring mechanisms and varying alternative hypotheses. For the purpose of illustration, we also apply these procedures to analyse three real datasets.

Keywords:

• Generalized log-rank test
• interval-censored
• likelihood ratio test
• multiple imputation
• score test
• Turnbull's algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).