Adapting SIMEX to correct for bias due to interval‐censored outcomes in survival analysis with time‐varying exposure

Many clinical and epidemiological applications of survival analysis focus on interval‐censored events that can be ascertained only at discrete times of clinic visits. This implies that the values of time‐varying covariates are not correctly aligned with the true, unknown event times, inducing a bias in the estimated associations. To address this issue, we adapted the simulation‐extrapolation (SIMEX) methodology, based on assessing how the estimates change with the artificially increased time between clinic visits. We propose diagnostics to choose the extrapolating function. In simulations, the SIMEX‐corrected estimates reduced considerably the bias to the null and generally yielded a better bias/variance trade‐off than conventional estimates. In a real‐life pharmacoepidemiological application, the proposed method increased by 27% the excess hazard of the estimated association between a time‐varying exposure, representing the 2‐year cumulative duration of past use of a hypertensive medication, and the hazard of nonmelanoma skin cancer (interval‐censored events). These simulation‐based and real‐life results suggest that the proposed SIMEX‐based correction may help improve the accuracy of estimated associations between time‐varying exposures and the hazard of interval‐censored events in large cohort studies where the events are recorded only at relatively sparse times of clinic visits/assessments. However, these advantages may be less certain for smaller studies and/or weak associations.