Predicting Treatment Effects from Surrogate Endpoints in Historical Trials in First-Line Metastatic Castration-Resistant Prostate Cancer.

Surrogate endpoints are becoming increasingly important in health technology assessment, where decisions are based on complex cost-effectiveness models (CEMs) that require numerous input parameters. Daniels and Hughes Surrogate Model was used to predict missing effect estimates in randomized controlled trials (RCTs) evaluating first-line treatments in metastatic castration-resistant prostate cancer (mCRPC) patients. Network meta-analyses (NMAs) were conducted to assess the comparative efficacy of these treatments.