Competing risks data in clinical oncology.

Competing risks data analysis plays a critical role in the evaluation of clinical utility of specific cancer treatments and can inform the development of future treatment approaches. Although competing risks data are ubiquitous in cancer studies, competing risks data are infrequently recognized and competing risks data analysis is not commonly performed. Consequently, efficacy of specific treatments is often incompletely and inaccurately presented and thus study results may be interpreted improperly.

Three-component cure rate model for nonproportional hazards alternative in the design of randomized clinical trials.

Background: Cure rate models have been extensively studied and widely used in time-to-event data in cancer clinical trials.

Purpose: Although cure rate models based on the generalized exponential distribution have been developed, they have not been used in the design of randomized cancer clinical trials, which instead have relied exclusively on two-component exponential cure rate model with a proportional hazards (PH) alternative. In some studies, the efficacy of the experimental treatment is expected to emerge some time after randomization.