Doubly robust inference with censoring unbiased transformations

TL;DR

This paper introduces a new doubly robust method for inference with censored data, applicable to various survival analysis settings, offering improved efficiency and practical utility demonstrated through simulations and real data application.

Contribution

It extends doubly robust censoring unbiased transformations to complex censored data structures, ensuring rate double robustness and oracle efficiency with practical implementation.

Findings

Favorable performance in simulations compared to existing methods

Achieves rate double robustness and oracle efficiency

Demonstrates practical utility in regression discontinuity with censored data

Abstract

This paper extends doubly robust censoring unbiased transformations to a broad class of censored data structures under the assumption of coarsening at random and positivity. This includes the classic survival and competing risks setting, but also encompasses multiple events. A doubly robust representation for the conditional bias of the transformed data is derived. This leads to rate double robustness and oracle efficiency properties for estimating conditional expectations when combined with cross-fitting and linear smoothers. Simulation studies demonstrate favourable performance of the proposed method relative to existing approaches. An application of the methods to a regression discontinuity design with censored data illustrates its practical utility.