A Bayesian approach to benefit–risk assessment in clinical studies with longitudinal data. Issue 3 (3rd May 2020)

• Record Type: Journal Article
• Authors: Yan, Dongyan; Ahn, Chul; Azadeh, Shabnam; Atlas, Mourad; Tiwari, Ram
• Is Part Of: Journal of biopharmaceutical statistics. Volume 30:Issue 3(2020)
• Page Start: 574
• Page End: 591
• Publication Date: 2020-05-03
• Subjects: Dirichlet distribution -- Dirichlet process -- Gibbs sampling -- log-odds ratio model -- power prior -- two-level multinomial; Pharmacy -- Statistical methods -- Periodicals; Drugs -- Testing -- Statistical methods -- Periodicals; Biometry -- Periodicals; Biopharmaceutics -- Periodicals; Pharmacokinetics -- Periodicals; 615.19
• DOI: 10.1080/10543406.2020.1726370
• Languages: English
• ISSNs: 1054-3406

Abstract

ABSTRACT: Chuang-Stein et al. proposed a method for benefit–risk assessment by formulating a five-category multinomial random variable with the first four categories as a combination of benefit and risk, and the fifth category to include subjects who withdraw from study. In this article, we subdivide the single withdrawal category into four sub-categories to consider withdrawal for different reasons. To analyze eight-category data, we propose a two-level multivariate-Dirichlet Model to identify benefit–risk measures at the population level. For individual benefit–risk, we use a log-odds ratio model with Dirichlet process prior. Two methods are applied to a hypothetical clinical trial data for illustration.