Evaluating the performance of a resampling approach for internally validating the association between a time-dependent binary indicator and time-to-event outcome

Identifying clinical or biological risk factors for disease plays a critical role in enabling earlier disease diagnosis, prognostic outcomes assessment, and may inform disease prevention or monitoring practices. One framework commonly examined is understanding the association between a risk factor ever occurring in follow-up and the future risk of an outcome. If such an association is found, researchers are often asked to validate the finding. External validation is often infeasible, and validation may only be performed internally. However, the performance of internal validation methods in the setting of a time-dependent binary indicator and a time-to-event outcome has not been well-studied. We emulated a dataset motivated by real-world serial biomarker observations and performed extensive simulation studies to evaluate the performance of a resampling-based method to internally validate the association between a time-dependent binary indicator and a time-to-event outcome. We found the resampling-based method achieved optimal power for validating such an association while maintaining good Type I error control.