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Adaptive treatment allocation for comparative clinical studies with recurrent events data
Author(s) -
Gao Jingya,
Su PeiFang,
Hu Feifang,
Cheung Siu Hung
Publication year - 2020
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13117
Subject(s) - contrast (vision) , computer science , sample size determination , event (particle physics) , binomial distribution , binomial (polynomial) , negative binomial distribution , statistics , econometrics , mathematical optimization , mathematics , artificial intelligence , physics , quantum mechanics , poisson distribution
Abstract In long‐term clinical studies, recurrent event data are sometimes collected and used to contrast the efficacies of two different treatments. The event reoccurrence rates can be compared using the popular negative binomial model, which incorporates information related to patient heterogeneity into a data analysis. For treatment allocation, a balanced approach in which equal sample sizes are obtained for both treatments is predominately adopted. However, if one treatment is superior, then it may be desirable to allocate fewer subjects to the less‐effective treatment. To accommodate this objective, a sequential response‐adaptive treatment allocation procedure is derived based on the doubly adaptive biased coin design. Our proposed treatment allocation schemes have been shown to be capable of reducing the number of subjects receiving the inferior treatment while simultaneously retaining a test power level that is comparable to that of a balanced design. The redesign of a clinical study illustrates the advantages of using our procedure.