Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial

Abstract

In oncology clinical trials, the exact time of event occurrence such as tumor progression is usually unknown but the time interval within which the event occurs is known. The determination of such survival time can be subject to measurement error and influenced by the timing of scheduled assessment. Ignoring interval-censored survival time could lead to serious estimation bias. In addition, a crucial characteristic of interval-censored data is how frequently the measurement interval is taken, which directly determine the efficiency of statistical inference. Therefore, it is highly desirable to find statistical methods that are robust to different assessment frequencies. We compare conventional imputation-based approach with non-parametric approaches to handle interval-censored survival data. We apply these approaches to both hypothesis test and the estimations of hazard and survival functions. Empirical performance of these methods are assessed through extensive simulation studies with various sample sizes. A phase III randomized clinical trial on metastatic colorectal cancer is analyzed by using conventional approaches and non-parametric interval-censored analysis approaches. Out findings suggest that the phase III colorectal cancer clinical trial failed to show a clinical benefit of adding bevacizumab (B) to standard chemotherapy (CT), and the proposed non-parametric interval-censored analysis approaches outperforms the conventional approach for routine applications to oncology clinical trials to analyze interval-censored survival data.