Identifying Cancer Relapse Using SEER-Medicare Data

Identification of relapse from SEER-Medicare data using clinical algorithms is feasible for cancers where a majority of patients receive treatment for relapse, without a “watch and wait” strategy, and where that treatment is with a modality that can be detected in billing data.

Abstract

Identification of relapse from SEER-Medicare data using clinical algorithms is feasible for cancers where a majority of patients receive treatment for relapse, without a "watch and wait" strategy, and where that treatment is with a modality that can be detected in billing data (ie, intravenous chemotherapy, radiation, surgery, or all three). Optimal analytic situations are ones in which the investigator is mostly interested in positive predictive value, less interested in sensitivity, and wants to evaluate outcomes among those patients who receive treatment for their relapsed disease. However, the accuracy of such an approach for cancers other than AML has not yet been established.