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

Yeqian Liu,Junyu Chen

doi:10.11648/j.bsi.20210601.13

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.

Highlights

Interval-censored time-to-event data occur naturally and frequently in randomized clinical trials, where the exact time of event occurrence is unknown but the time interval within which the event occurs is known
Based on our extensive simulations, when maximum number of assessment is fewer than 5 times, conventional method would have about at least 10% negative bias at log hazard ratio scale, while for Finkelstein’s method, the bias is at most 5%
We apply the methodologies proposed in previous sections to a Phase III colorectal cancer clinical trial (ITACa)

Summary

Introduction

Interval-censored time-to-event data occur naturally and frequently in randomized clinical trials, where the exact time of event occurrence is unknown but the time interval within which the event occurs is known. The left-point of the time interval in the interval-censored data represents the last time the individual is known to be event-free, and the right-point of the interval represents the earliest time that the individual is recorded with an event. There are two important special cases of interval-censored data. The first case is current status data, where only the observation time and whether or not the event has occurred at the time are known. The second case is grouped time-to-event data, where the interval-censored time for each subject is a member of a collection of non-overlapping intervals, and multinomial distribution can be used on the number of subjects in the given intervals. This paper focuses on case II interval-censored data

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