An Empirical Investigation of Bayesian Clinical Trial Design in Metastatic Breast Cancer
| Autor: | Jiaxi Zhou, Shenghui Tang, Susan Jin, Rajeshwari Sridhara, Erik Bloomquist |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Bayesian probability Breast Neoplasms Analysis of clinical trials Machine learning computer.software_genre 030226 pharmacology & pharmacy 01 natural sciences law.invention 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Breast cancer Randomized controlled trial law Prior probability medicine Humans Pharmacology (medical) 0101 mathematics Pharmacology Toxicology and Pharmaceutics (miscellaneous) Clinical Trials as Topic business.industry Clinical study design Public Health Environmental and Occupational Health Bayes Theorem medicine.disease Confidence interval Clinical trial Artificial intelligence business computer |
| Zdroj: | Therapeutic innovationregulatory science. 54(4) |
| ISSN: | 2168-4804 |
| Popis: | Over the past 20 years, there has been increasing interest in the use of Bayesian statistical methods for the analysis of clinical trials used to support regulatory decisions. Bayesian methods for the analysis of clinical trials are an attractive option when good prior information is available. Yet, in many cases, prior information is scarce and only tentative or proprietary prior information exists. In these situations, it is necessary to use noninformative type or skeptical-type priors. We undertook a systematic study of Bayesian methods and applied them to 13 randomized clinical trials in metastatic breast cancer submitted to the U.S. Food and Drug Administration for registrational purposes. Across all 13 studies, there were a total of 10,521 patients using 10 experimental agents. Our results demonstrated that Bayesian analyses with noninformative priors provided similar results to more traditional analyses. We also found that early interim looks at the study results can vary widely based upon the type of prior used. Finally, we found that pre-defined threshold stopping rules need to be relatively strong to prevent trials from stopping very early. Our results suggest that, when prior information is limited and a noninformative prior is used, there is little numerical difference between Bayesian methods and more traditional analysis methods. Bayesian methods, however, may provide additional summaries of the data that are more easily interpretable than means and confidence intervals. |
| Databáze: | OpenAIRE |
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