Instrumental variable estimation of complier casual treatment effects with interval-censored competing risks data.

This paper discusses the assessment of causal treatment effects on a time-to-event outcome, a crucial part of many scientific investigations. Although some methods have been developed for the problem, they are not applicable to situations where there exist both interval censoring and competing risks. We fill in this critical gap under a class of transformation models for cumulative incidence functions by developing an instrumented variable (IV) estimation approach. The IV is a valuable tool commonly used to mitigate the impact of endogenous treatment selection and to determine causal treatment effects in an unbiased manner. The proposed method is flexible as the model includes many commonly used models such as the sub-distributional proportional odds and hazards models (ie, the Fine-Gray model) as special cases. The resulting estimator for the regression parameter is shown to be consistent and asymptotically normal. A simulation study is conducted to evaluate finite sample performance of the proposed approach and suggests that it works well in practice. It is applied to a breast cancer screening study.