Analysis of composite endpoints with component‐wise censoring in the presence of differential visit schedules

Abstract

Composite endpoints are very common in clinical research, such as recurrence‐free survival in oncology research, defined as the earliest of either death or disease recurrence. Because of the way data are collected in such studies, component‐wise censoring is common, where, for example, recurrence is an interval‐censored event and death is a right‐censored event. However, a common way to analyze such component‐wise censored composite endpoints is to treat them as right‐censored, with the date at which the non‐fatal event was detected serving as the date the event occurred. This approach is known to introduce upward bias when the Kaplan‐Meier estimator is applied, but has more complex impact on semi‐parametric regression approaches. In this article we compare the performance of the Cox model estimators for right‐censored data and the Cox model estimators for interval‐censored data in the context of component‐wise censored data where the visit process differs across levels of a covariate of interest, a common scenario in observational data. We additionally examine estimators of the cause‐specific hazard when applied to the individual components of such component‐wise censored composite endpoints. We found that when visit schedules differed according to levels of a covariate of interest, the Cox model estimators for right‐censored data and the estimators for cause‐specific hazards were increasingly biased as the frequency of visits decreased. The Cox model estimator for interval‐censored data with censoring at the last disease‐free date is recommended for use in the presence of differential visit schedules.

Performance of Cox regression models for composite time-to-event endpoints with component-wise censoring in randomized trials

“…Based on the Cox–Aalen model, simulations in Boruvka and Cook 40 indicated better performance with selecting the midpoint under scenarios with mixed censoring. Conversely, simulations focusing on component-wise censoring and different visit schedules in Eaton and Zabor 27 suggest selecting the last-known disease-free state. However, both of these studies considered smaller sample sizes (up to N = 400 or 500) and therefore may not be applicable for larger clinical trials such as PREVENTABLE.…”

“…For interval-censored outcomes, it is unknown if the midpoint of the interval or the upper value of the interval should be used to calculate the composite outcome. A recent paper by Eaton and Zabor 27 compared Cox regression models with multi-state models for component-wise censoring, but this paper focused on differential visit schedules and used relatively small sample sizes of 100, 200, and 400. A simulation study suggested that Cox model estimates had adequate power and low bias when the upper value of the interval-censored component was used in the composite outcome.…”

Immunosuppression versus Supportive Care on Kidney Outcomes in IgA Nephropathy in the Real-World Setting

“…In this case, assuming the survival data as right-censored may induce bias when the Kaplan–Meier estimator is applied. 17 For this reason, we treated the time to the composite event as interval-censored, with the interval being bounded by the last visit that the event was not observed and the first visit that the event was observed in the patients who experienced the event or infinity in those who did not experience the event. We used a nonparametric method to estimate the curves of cumulative incidence of the composite outcomes stratified by the two treatment arms in the matched cohort.…”