Bayesian nonparametric statistics: A new toolkit for discovery in cancer research. (4th July 2017)

• Record Type: Journal Article
• Authors: Thall, Peter F.; Mueller, Peter; Xu, Yanxun; Guindani, Michele
• Is Part Of: Pharmaceutical statistics. Volume 16:Number 6(2017)
• Page Start: 414
• Page End: 423
• Publication Date: 2017-07-04
• Subjects: Bayesian nonparametric statistics -- clinical trial design -- density estimation -- dynamic treatment regime -- targeted therapy; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.1819
• Languages: English
• ISSNs: 1539-1604

Abstract

Abstract : Many commonly used statistical methods for data analysis or clinical trial design rely on incorrect assumptions or assume an over‐simplified framework that ignores important information. Such statistical practices may lead to incorrect conclusions about treatment effects or clinical trial designs that are impractical or that do not accurately reflect the investigator's goals. Bayesian nonparametric (BNP) models and methods are a very flexible new class of statistical tools that can overcome such limitations. This is because BNP models can accurately approximate any distribution or function and can accommodate a broad range of statistical problems, including density estimation, regression, survival analysis, graphical modeling, neural networks, classification, clustering, population models, forecasting and prediction, spatiotemporal models, and causal inference. This paper describes 3 illustrative applications of BNP methods, including a randomized clinical trial to compare treatments for intraoperative air leaks after pulmonary resection, estimating survival time with different multi‐stage chemotherapy regimes for acute leukemia, and evaluating joint effects of targeted treatment and an intermediate biological outcome on progression‐free survival time in prostate cancer.