Interval-Censoring in Biomedical and Biopharmaceutical Clinical Trials

 In some biomedical clinical trials, especially cancer clinical trials, time-to-event data are generated from cancer scan within a specific interval (such as 1 or more months) resulting in interval-censored data. The common practice in analyzing data from this type of trial is to approximate the interval-censored data using the midpoint or right endpoint (i.e. the first observed time) of the interval so that well-known statistical methods developed for right-censored data may be used for the requisite analyses. This could introduce bias and lead to erroneous conclusions. In this paper we conduct a simulation study to investigate the bias in parameter estimation and inference. Appropriate statistical models for interval-censored data are proposed with application to breast cancer clinical trial data.