Doubly robust inference with censoring unbiased transformations

Fuente: arXiv
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Main Author: Sandqvist, Oliver Lunding
Format: Preprint
Published: 2024
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author Sandqvist, Oliver Lunding
author_facet Sandqvist, Oliver Lunding
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2411_04909
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Doubly robust inference with censoring unbiased transformations
Sandqvist, Oliver Lunding
Methodology
Statistics Theory
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.
title Doubly robust inference with censoring unbiased transformations
topic Methodology
Statistics Theory
url https://arxiv.org/abs/2411.04909