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

Abstract

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. Databases were searched (inception to October 2022) using Ovid®. Several grey literature searches were also conducted (PROSPERO: CRD42021283512). Available trial data for radiographic progression-free survival (rPFS) and overall survival (OS) were used to predict the unreported effect of rPFS or OS for relevant comparator treatments. Bayesian NMAs were conducted using observed and predicted treatment effects. Effect estimates and 95% credible intervals were calculated for each comparison. Mean ranks and the probability of being best (p-best) were obtained. Twenty-five RCTs met the eligibility criteria and of these, 8 reported jointly rPFS and OS; while rPFS was predicted for 12 RCTs and 10 comparators, and OS was predicted for 5 RCTs and 6 comparators. A nonstandard dose of docetaxel (docetaxel 50 mg/m² every 2 weeks) had the highest probability of being the most effective for rPFS (p-best: 59%) and OS (p-best: 48%), followed by talazoparib plus enzalutamide (13% and 19%, respectively). Advanced surrogate modelling techniques allowed obtaining relevant parameter and indirect estimates of previously unavailable data and may be used to populate future CEMs requiring rPFS and OS in first-line mCRPC.