[Skip to Content]
[Skip to Content Landing]
Views 347
Citations 0
Original Investigation
April 13, 2017

Use of Bayesian Decision Analysis to Minimize Harm in Patient-Centered Randomized Clinical Trials in Oncology

Author Affiliations
  • 1Laboratory for Financial Engineering, MIT Sloan School of Management, Cambridge, Massachusetts
  • 2Department of Computer Science, Boston College, Chestnut Hill, Massachusetts
  • 3Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts
  • 4Mayo Clinic, Rochester, Minnesota
  • 5Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts
  • 6AlphaSimplex Group LLC, Cambridge, Massachusetts
JAMA Oncol. Published online April 13, 2017. doi:10.1001/jamaoncol.2017.0123
Key Points

Question  How can patient preferences and burden of disease be explicitly incorporated into randomized clinical trials (RCTs) in oncology and what is the impact on statistical thresholds for drug approval?

Findings  In this analysis, Bayesian decision analysis (BDA) was applied to a data set of 10 clinical trials from the Alliance for Clinical Trials in Oncology. The BDA-optimal alphas were often much larger than 2.5% for terminal cancers with short survival times and no effective therapies (eg, pancreatic cancer) and smaller than 2.5% for less serious cancers with long survival times, several effective therapies, and high prevalence.

Meaning  Bayesian decision analysis can be applied to RCTs by choosing a sample size (n) and type 1 error rate (alpha) to minimize the overall expected harm to current and future patients, where expected harm is computed under both null and alternative hypotheses.


Importance  Randomized clinical trials (RCTs) currently apply the same statistical threshold of alpha = 2.5% for controlling for false-positive results or type 1 error, regardless of the burden of disease or patient preferences. Is there an objective and systematic framework for designing RCTs that incorporates these considerations on a case-by-case basis?

Objective  To apply Bayesian decision analysis (BDA) to cancer therapeutics to choose an alpha and sample size that minimize the potential harm to current and future patients under both null and alternative hypotheses.

Data Sources  We used the National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) database and data from the 10 clinical trials of the Alliance for Clinical Trials in Oncology.

Study Selection  The NCI SEER database was used because it is the most comprehensive cancer database in the United States. The Alliance trial data was used owing to the quality and breadth of data, and because of the expertise in these trials of one of us (D.J.S.).

Data Extraction and Synthesis  The NCI SEER and Alliance data have already been thoroughly vetted. Computations were replicated independently by 2 coauthors and reviewed by all coauthors.

Main Outcomes and Measures  Our prior hypothesis was that an alpha of 2.5% would not minimize the overall expected harm to current and future patients for the most deadly cancers, and that a less conservative alpha may be necessary. Our primary study outcomes involve measuring the potential harm to patients under both null and alternative hypotheses using NCI and Alliance data, and then computing BDA-optimal type 1 error rates and sample sizes for oncology RCTs.

Results  We computed BDA-optimal parameters for the 23 most common cancer sites using NCI data, and for the 10 Alliance clinical trials. For RCTs involving therapies for cancers with short survival times, no existing treatments, and low prevalence, the BDA-optimal type 1 error rates were much higher than the traditional 2.5%. For cancers with longer survival times, existing treatments, and high prevalence, the corresponding BDA-optimal error rates were much lower, in some cases even lower than 2.5%.

Conclusions and Relevance  Bayesian decision analysis is a systematic, objective, transparent, and repeatable process for deciding the outcomes of RCTs that explicitly incorporates burden of disease and patient preferences.