Bayesian clinical trials at The University of Texas MD Anderson Cancer Center: An update

Abstract

Background/aims In our 2009 article, we showed that Bayesian methods had established a foothold in developing therapies in our institutional oncology trials. In this article, we will document what has happened since that time. In addition, we will describe barriers to implementing Bayesian clinical trials, as well as our experience overcoming them. Methods We reviewed MD Anderson Cancer Center clinical trials submitted to the institutional protocol office for scientific and ethical review between January 2009 and December 2013, the same length time period as the previous article. We tabulated Bayesian methods implemented for design or analyses for each trial and then compared these to our previous findings. Results Overall, we identified 1020 trials and found that 283 (28%) had Bayesian components so we designated them as Bayesian trials. Among MD Anderson–only and multicenter trials, 56% and 14%, respectively, were Bayesian, higher rates than our previous study. Bayesian trials were more common in phase I/II trials (34%) than in phase III/IV (6%) trials. Among Bayesian trials, the most commonly used features were for toxicity monitoring (65%), efficacy monitoring (36%), and dose finding (22%). The majority (86%) of Bayesian trials used non-informative priors. A total of 75 (27%) trials applied Bayesian methods for trial design and primary endpoint analysis. Among this latter group, the most commonly used methods were the Bayesian logistic regression model (N = 22), the continual reassessment method (N = 20), and adaptive randomization (N = 16). Median institutional review board approval time from protocol submission was the same 1.4 months for Bayesian and non-Bayesian trials. Since the previous publication, the Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial was the first large-scale decision trial combining multiple treatments in a single trial. Since then, two regimens in breast cancer therapy have been identified and published from the cooperative Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis (I-SPY 2), enhancing cooperation among investigators and drug developers across the nation, as well as advancing information needed for personalized medicine. Many software programs and Shiny applications for Bayesian trial design and calculations are available from our website which has had more than 21,000 downloads worldwide since 2004. Conclusion Bayesian trials have the increased flexibility in trial design needed for personalized medicine, resulting in more cooperation among researchers working to fight against cancer. Some disadvantages of Bayesian trials remain, but new methods and software are available to improve their function and incorporation into cancer clinical research.

Factors influencing the statistical planning, design, conduct, analysis and reporting of trials in health care: A systematic review

“…The literature describes skillsets statisticians are required to have, including good communication skills [ 31 , [39] , [40] , [41] ], have good “customer service, management, and interpersonal skills” , consultancy skills, collaborative, negotiation, entrepreneurial, written and oral skills, deliver their tasks on time, adhere to allocated budgets, have problem-solving skills, and can be responsive and be “ creative, innovative and flexible” [ 39 , [42] , [43] , [44] ]. While statisticians are called to have leadership skills [ 39 , 45 , 46 ], one author [ 27 ] describes how the project statistician interacts with the trial leadership, but unlike the other authors, does not refer to the statistician themselves becoming a leader.…”

Current status of Bayesian clinical trials for oncology, 2020

“…Published literature like our review is limited for comparing obtained results with those of other authors, but there is a research performed at the MD Anderson Cancer Center that has studied Bayesian approach in their clinical trials in 2019. They reported that Bayesian trials were more common in phase I/II trials [ 16 ].…”

Moving Beyond 3+3: The Future of Clinical Trial Design

“…Despite plenty of literature demonstrating superiority of model-based or model-assisted designs compared with traditional 3+3 design in reliability, safety, and flexibility, 23 the application of novel Bayesian designs in oncology drug development is still limited. Although an increasing proportion of Bayesian designs has been shown in some institutions, 46 overall, the impact of statistical models on development of approved drug has not been expanded yet. 47 A powerful novel design could not benefit any patient or drug if it is not applied in real trial and drug development.…”