Strength of statistical evidence for the efficacy of cancer drugs: a Bayesian reanalysis of randomized trials supporting Food and Drug Administration approval

Objectives: To quantify the strength of statistical evidence of randomized controlled trials (RCTs) for novel cancer drugs approved by the Food and Drug Administration in the last 2 decades.

Study Design and Setting: We used data on overall survival (OS), progression-free survival, and tumor response for novel cancer drugs approved for the first time by the Food and Drug Administration between January 2000 and December 2020. We assessed strength of statistical evidence by calculating Bayes factors (BFs) for all available endpoints, and we pooled evidence using Bayesian fixed-effect meta-analysis for indications approved based on 2 RCTs. Strength of statistical evidence was compared among endpoints, approval pathways, lines of treatment, and types of cancer.

Results: We analysed the available data from 82 RCTs corresponding to 68 indications supported by a single RCT and 7 indications supported by 2 RCTs. Median strength of statistical evidence was ambiguous for OS (BF = 1.9; interquartile range [IQR] 0.5–14.5), and strong for progression-free survival (BF = 24,767.8; IQR 109.0–7.3 × 10⁶) and tumor response (BF = 113.9; IQR 3.0–547,100). Overall, 44 indications (58.7%) were approved without clear statistical evidence for OS improvements and 7 indications (9.3%) were approved without statistical evidence for improvements on any endpoint. Strength of statistical evidence was lower for accelerated approval compared to nonaccelerated approval across all 3 endpoints. No meaningful differences were observed for line of treatment and cancer type. This analysis is limited to statistical evidence. We did not consider nonstatistical factors (eg, risk of bias, quality of the evidence).

Conclusion: BFs offer novel insights into the strength of statistical evidence underlying cancer drug approvals. Most novel cancer drugs lack strong statistical evidence that they improve OS, and a few lack statistical evidence for efficacy altogether. These cases require a transparent and clear explanation. When evidence is ambiguous, additional postmarketing trials could reduce uncertainty.