Can Oncologists Predict the Efficacy of Treatments in Randomized Trials?. (26th August 2020)

• Record Type: Journal Article
• Authors: Benjamin, Daniel M.; Mandel, David R.; Barnes, Tristan; Krzyzanowska, Monika K.; Leighl, Natasha; Tannock, Ian F.; Kimmelman, Jonathan
• Is Part Of: Oncologist. Volume 26:Number 1(2021)
• Page Start: 56
• Page End: 62
• Publication Date: 2020-08-26
• Subjects: Forecasting -- Cancer -- Clinical trial -- Drug development -- Bioethics -- Medical ethics; Oncology -- Periodicals; Tumors -- Periodicals; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Oncology; Tumors; Neoplasms; Electronic journals; Periodicals; Periodicals; 616.994
• DOI: 10.1634/theoncologist.2020-0054
• Languages: English
• ISSNs: 1083-7159

Abstract

Abstract: Background: Decisions about trial funding, ethical approval, or clinical practice guideline recommendations require expert judgments about the potential efficacy of new treatments. We tested whether individual and aggregated expert opinion of oncologists could predict reliably the efficacy of cancer treatments tested in randomized controlled trials. Materials and Methods: An international sample of 137 oncologists specializing in genitourinary, lung, and colorectal cancer provided forecasts on primary outcome attainment for five active randomized cancer trials within their subspecialty; skill was assessed using Brier scores (BS), which measure the average squared deviation between forecasts and outcomes. Results: A total of 40% of trials in our sample reported positive primary outcomes. Experts generally anticipated this overall frequency (mean forecast, 34%). Individual experts on average outperformed random predictions (mean BS = 0.29 [95% confidence interval (CI), 0.28–0.33] vs. 0.33) but underperformed prediction algorithms that always guessed 50% (BS = 0.25) or that were trained on base rates (BS = 0.19). Aggregating forecasts improved accuracy (BS = 0.25; 95% CI, 0.16–0.36]). Neither individual experts nor aggregated predictions showed appreciable discrimination between positive and nonpositive trials (area under the curve of a receiver operating characteristic curve, 0.52 and 0.43, respectively). Conclusion: These findings are based on a limited sample of trials. However, they reinforce the importance of basing research and policy decisions on the results of randomized trials rather than expert opinion or low‐level evidence. Implications for Practice: Predictions of oncologists, either individually or in the aggregate, did not anticipate reliably outcomes for randomized trials in cancer. These findings suggest that pooled expert opinion about treatment efficacy is no substitute for randomized trials. They also underscore the challenges of using expert opinion to prioritize interventions for clinical trials or to make recommendations in clinical practice guidelines. Abstract : The effectiveness of decisions regarding approval of clinical trials relies partly on the ability of approvers to assess accurately the promise of treatments and prioritize those most promising. This study measured the ability of oncologist to predict whether cancer treatments would demonstrate efficacy in randomized trials.