Learning Causal Hazard Ratio with Endogeneity
share
Cox's proportional hazards model is one of the most popular statistical models to evaluate associations of a binary exposure with a censored failure time outcome. When confounding factors are not fully observed, the exposure hazard ratio estimated using a Cox model is not causally interpretable. To address this, we propose novel approaches for identification and estimation of the causal hazard ratio in the presence of unmeasured confounding factors. Our approaches are based on a binary instrumental variable and an additional no-interaction assumption. We derive, to the best of our knowledge, the first consistent estimator of the population marginal causal hazard ratio within an instrumental variable framework. Our estimator admits a closed-form representation, and hence avoids the drawbacks of estimating equation based estimators. Our approach is illustrated via simulation studies and a data analysis.
READ FULL TEXT
