Analyzing cohort studies with interval-censored data: A new model-based linear rank-type test

To compare two or more survival distributions with interval-censored data, various nonparametric tests have been proposed. Some are based on the $$ -family introduced by Harrington and Fleming (1991) that allows flexibility for situations in which the hazard ratio decreases monotonically to unity. However, it is unclear how to choose the appropriate value of the parameter $$ . In this work, we propose a novel linear rank-type test for analyzing interval-censored data that derived from a proportional reversed hazard model. We show its relationship with decreasing hazard ratio. This test statistic provides an alternative to the $$ -based test statistics by bypassing the choice of the $$ parameter. Simulation results show its good behavior. Two studies on breast cancer and drug users illustrate its practical uses and highlight findings that would have been overlooked if other tests had been used. The test is easy to implement with standard software and can be used for a wide range of situations with interval-censored data to test the equality of survival distributions between two or more independent groups.