Exposure‐Response Analysis for Time‐to‐Event Data in the Presence of Adaptive Dosing: Efficient Approaches and Pitfalls

Analyzing exposure-response (E-R) relationships for time-to-event (TTE) endpoints presents challenges due to the inherent time-dependent nature of the data. Some authors address these difficulties by using a fixed timepoint approach, where exposure is assessed at a predetermined time rather than dynamically over time. (e.g., initial exposure or last exposure). The aim of the current work is to compare the use of time-static and time-varying metrics to assess the E-R relationship through simulations. PK exposures were simulated from a one-compartment model and TTE data from a parametric proportional hazard model, involving the weekly average PK concentration as a time-varying covariate. Several scenarios were considered to handle the type of dosing (fixed or adaptive), the accumulation of the drug (low or strong), the type of event (efficacy, safety or independent), and the timing of the event onset (early or late). Wald tests on the exposure effect parameter were performed to assess the significance of the E-R relationship. For each simulation scenario, the type-I error and the power of the Wald tests were reported, revealing that no time-static metric consistently produced reliable results across all conditions. In order to ensure adequate statistical properties, we recommend using time-varying exposure, which shows good performance across all scenarios.