Proximal Survival Analysis to Handle Dependent Right Censoring
Andrew Ying

TL;DR
This paper introduces a nonparametric framework for survival analysis that accounts for dependent right censoring by treating covariates as imperfect proxies, providing adaptive estimators with robustness and applying them to real-world data.
Contribution
It develops a novel nonparametric identification framework that relaxes the assumption of independent censoring, incorporating proxy covariates and proposing consistent, asymptotically normal estimators.
Findings
Estimators are shown to be consistent and asymptotically normal.
Simulation studies demonstrate finite-sample performance.
Application to SEER-Medicare data illustrates practical utility.
Abstract
Many epidemiological and clinical studies aim at analyzing a time-to-event endpoint. A common complication is right censoring. In some cases, it arises because subjects are still surviving after the study terminates or move out of the study area, in which case right censoring is typically treated as independent or non-informative. Such an assumption can be further relaxed to conditional independent censoring by leveraging possibly time-varying covariate information, if available, assuming censoring and failure time are independent among covariate strata. In yet other instances, events may be censored by other competing events like death and are associated with censoring possibly through prognoses. Realistically, measured covariates can rarely capture all such associations with certainty. For such dependent censoring, often covariate measurements are at best proxies of underlying…
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Taxonomy
TopicsStatistical Methods and Inference · Statistical Methods and Bayesian Inference · Advanced Causal Inference Techniques
