In this paper, we lay out the basic elements of Bayesian sample size determination (SSD) for the Bayesian design of a two-arm superiority clinical trial. We develop a flowchart of the Bayesian SSD that highlights the critical components of a Bayesian design and provides a practically useful roadmap for designing a Bayesian clinical trial in real world applications. We empirically examine the amount of borrowing, the choice of noninformative priors, and the impact of model misspecification on the Bayesian type I error and power. A formal and statistically rigorous formulation of conditional borrowing within the decision rule framework is developed. Moreover, by extending the partial borrowing power priors, a new borrowing-by-parts power prior for incorporating historical data is proposed. Computational algorithms are also developed to calculate the Bayesian type I error and power. Extensive simulation studies are carried out to explore the operating characteristics of the proposed Bayesian design of a superiority trial.
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http://dx.doi.org/10.1080/19466315.2022.2090429 | DOI Listing |
Surv Methodol
December 2024
Department of Statistical Science, 214a Old Chemistry Building, Duke University, Durham, NC 27708-0251.
When seeking to release public use files for confidential data, statistical agencies can generate fully synthetic data. We propose an approach for making fully synthetic data from surveys collected with complex sampling designs. Our approach adheres to the general strategy proposed by Rubin (1993).
View Article and Find Full Text PDFJ Biopharm Stat
January 2025
Department of Biostatistics, University of North Carolina, Chapel Hill, USA.
With the continuous advancement of medical treatments, there is an increasing demand for clinical trial designs and analyses using cure rate models to accommodate a plateau in the survival curve. This is especially pertinent in oncology, where high proportions of patients, such as those with melanoma, lung cancer, and endometrial cancer, exhibit usual life spans post-cancer detection. A Bayesian clinical trial design methodology for multivariate time-to-event outcomes with cured fractions is developed.
View Article and Find Full Text PDFBiostatistics
December 2024
Department of Statistical Sciences, College of Arts and Sciences, Wake Forest University, 127 Manchester Hall, Winston-Salem, NC, 27109, United States.
The opioid epidemic is a significant public health challenge in North Carolina, but limited data restrict our understanding of its complexity. Examining trends and relationships among different outcomes believed to reflect opioid misuse provides an alternative perspective to understand the opioid epidemic. We use a Bayesian dynamic spatial factor model to capture the interrelated dynamics within six different county-level outcomes, such as illicit opioid overdose deaths, emergency department visits related to drug overdose, treatment counts for opioid use disorder, patients receiving prescriptions for buprenorphine, and newly diagnosed cases of acute and chronic hepatitis C virus and human immunodeficiency virus.
View Article and Find Full Text PDFStat Med
February 2025
Biostatistics, Innovatio Statistics Inc., Bridgewater, New Jersey, USA.
Sample size re-estimation (SSR) is perhaps the most used adaptive procedure in both frequentist and Bayesian adaptive designs for clinical trials. The primary focus of all current frequentist and Bayesian SSR procedures is type I error control. We propose a hybrid frequentist-Bayesian SSR approach that focuses on optimizing operating characteristics (OC), which uses simulations to investigate the associated OC and adjusts accordingly.
View Article and Find Full Text PDFJ Comput Chem
January 2025
RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
QCforever is a wrapper designed to automatically and simultaneously calculate various physical quantities using quantum chemical (QC) calculation software for blackbox optimization in chemical space. We have updated it to QCforever2 to search the conformation and optimize density functional parameters for a more accurate and reliable evaluation of an input molecule. In blackbox optimization, QCforever2 can work as compactly arranged surrogate models for costly chemical experiments.
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