Endpoint surrogacy in oncological randomized controlled trials with immunotherapies: a systematic review of trial-level and arm-level meta-analyses.

Few cancer drugs or their indications achieved survival benefit in subsequent trials during postmarket period after approval based on surrogate endpoints. This causes a concern of using surrogate endpoints instead of overall survival (OS) as the primary endpoint for trial design, implementation and regulation approval. We conducted a systematic review to summarize the findings from published meta-analyses which have evaluated endpoint surrogacy for OS in oncological randomized controlled trials (RCTs) with immunotherapies. After searching articles indexed in PubMed prior to 24 February 2019, we identified a total of 11 meta-analyses for advanced multiple tumors, non-small cell lung cancer (NSCLC), urothelial carcinoma, renal cell carcinoma, melanoma; most (91%; 10/11) focused on immune checkpoint inhibitors. Although the evaluation criteria adopted by these meta-analyses for validating endpoint surrogacy were not consistent (ranging from R2 ≥0.60 to R2 ≥0.80), the results were consistent. Few studies show an association between OS and progression-free survival (PFS)/objective response rate (ORR) that met the lowest evaluation criteria (R2 ≥0.60), based on treatment effects (8%; 2/26 indications) or absolute results from experimental arm (0%; 0/11 indications). However, the association between OS and 1-year survival rate met the lowest criteria based on both the trial-level results (4/4 indications) and the arm-level results (5/5 indications). In lieu of this finding, we are supportive of an alternative endpoint, e.g., 1-year survival rate, rather than the more conventional choices PFS and ORR, as promising surrogate endpoint for OS in immunotherapy RCTs. We encourage further investigation on endpoint surrogacy based on the same or different settings, especially an assessment on survival rate at milestone time (e.g., 1-year), which has been demonstrated valuable for predicting OS in meta-analyses.

Duke Scholars

Author Xiaofei Wang Biostatistics & Bioinformatics, Division of Biostatistics