Causal Proportional Hazards Estimation with a Binary Instrumental Variable.

ABSTRACT: Instrumental variables (IV) are a useful tool for estimating causal effects in the presence of unmeasured confounding. IV methods are well developed for uncensored outcomes, particularly for structural linear equation models, where simple two-stage estimation schemes are available. The extension of these methods to survival settings is challenging, partly because of the nonlinearity of the popular survival regression models and partly because of the complications associated with right censoring or other survival features. Motivated by the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer screening trial, we develop a simple causal hazard ratio estimator in a proportional hazards model with right censored data. The method exploits a special characterization of IV which enables the use of

SUBMITTER: Kianian B 

PROVIDER: S-EPMC8716008 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature