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No unmeasured confounding is often assumed in estimating treatment effects inobservational data when using approaches such as propensity scores and inverseprobability weighting. However, in many such studies due to the limitation ofthe databases, collected confounders are not exhaustive, and it is crucial toexamine the extent to which the resulting estimate is sensitive to theunmeasured confounders. We consider this problem for survival and competingrisks data. Due to the complexity of models for such data, we adapt thesimulated potential confounders approach of Carnegie et al. (2016), whichprovides a general tool for sensitivity analysis due to unmeasured confounding.More specifically, we specify one sensitivity parameter to quantify theassociation between an unmeasured confounder and the treatment assignment, andanother set of parameters to quantify the association between the confounderand the time-to-event outcomes. By varying the magnitudes of the sensitivityparameters, we estimate the treatment effect of interest using the stochasticEM and the EM algorithms. We demonstrate the performance of our methods onsimulated data, and apply them to a comparative effectiveness study ininflammatory bowel disease (IBD).

Sensitivity Analysis of Treatment Effect to Unmeasured Confounding in Observational Studies with Survival and Competing Risks Outcomes