Improving Efficiency and Robustness of the Prognostic Accuracy of Biomarkers With Partial Incomplete Failure-Time Data and Auxiliary Outcome: Application to Prostate Cancer Active Surveillance Study.

Abstract

When novel biomarkers are developed for the clinical management of patients diagnosed with cancer, it is critical to quantify the accuracy of a biomarker-based decision tool. The evaluation can be challenging when the definite outcome $T$ , such as time to disease progression, is only partially ascertained on a limited set of study patients. Under settings where $T$ is only observed on a subset but an auxiliary outcome correlated with $T$ is available on all subjects, we propose an augmented estimation procedure for commonly used time-dependent accuracy measures. The augmented estimators are easy to implement without imposing modeling assumptions between the two types of time-to-event outcomes and are more efficient than the complete-case estimator. When the ascertainment of the outcome is non-random and subject to informative censoring, we further augment our proposed method with inverse probability weighting to improve robustness. Results from simulation studies confirm the robustness and efficiency properties of the proposed estimators. The method is illustrated with data from the Canary Prostate Active Surveillance Study.