Performance of Cox proportional hazard models on recovering the ground truth of confounded exposure–response relationships for large‐molecule oncology drugs

A Cox proportional hazard (CoxPH) model is conventionally used to assess exposure–response (E–R), but its performance to uncover the ground truth when only one dose level of data is available has not been systematically evaluated. We established a simulation workflow to generate realistic E–R datasets to assess the performance of the CoxPH model in recovering the E–R ground truth in various scenarios, considering two potential reasons for the confounded E–R relationship. We found that at high doses, when the pharmacological effects are largely saturated, missing important confounders is the major reason for inferring false‐positive E–R relationships. At low doses, when a positive E–R slope is the ground truth, either missing important confounders or mis‐specifying the interactions can lead to inaccurate estimates of the E–R slope. This work constructed a simulation workflow generally applicable to clinical datasets to generate clinically relevant simulations and provide an in‐depth interpretation on the E–R relationships with confounders inferred by the conventional CoxPH model.