Estimating population-averaged hazard ratios in the presence of unmeasured confounding

The Cox regression model and its associated hazard ratio (HR) are frequently used for summarizing the effect of treatments on time to event outcomes. However, the HR's interpretation strongly depends on the assumed underlying survival model. The challenge of interpreting the HR has been the focus of a number of recent papers. Several alternative measures have been proposed in order to deal with these concerns. The marginal Cox regression models include an identifiable hazard ratio without individual but populational causal interpretation. In this work, we study the properties of one particular marginal Cox regression model and consider its estimation in the presence of omitted confounder from an instrumental variable-based procedure. We prove the large sample consistency of an estimation score which allows non-binary treatments. Our Monte Carlo simulations suggest that finite sample behavior of the procedure is adequate. The studied estimator is more robust than its competitor (Wang et al.) for weak instruments although it is slightly more biased for large effects of the treatment. The practical use of the presented techniques is illustrated through a real practical example using data from the vascular quality initiative registry. The used R code is provided as Supplementary material.

Automated summary

The Cox regression model and its associated hazard ratio (HR) are widely used for summarizing the effect of treatments on time to event outcomes. However, interpreting the HR depends on the assumed underlying survival model, which poses challenges. Alternative measures have been proposed to address these concerns. This study examines the properties of a specific marginal Cox regression model and considers its estimation when there is a omitted confounder.