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Two‐stage randomized trial design for testing treatment, preference, and self‐selection effects for count outcomes
Author(s) -
Shi Yu,
Cameron Briana,
Gu Xian,
Kane Michael,
Peduzzi Peter,
Esserman Denise A.
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8686
Subject(s) - preference , selection (genetic algorithm) , randomized controlled trial , sample size determination , test (biology) , research design , computer science , outcome (game theory) , clinical trial , completely randomized design , medicine , statistics , medical physics , mathematics , artificial intelligence , surgery , paleontology , mathematical economics , biology
While the traditional clinical trial design lays emphasis on testing the treatment effect between randomly assigned groups, it ignores the role of patient preference for a particular treatment in the trial. Yet, for healthcare providers who seek to optimize the patient‐centered treatment strategy, the evaluation of a patient's psychology toward each treatment could be a key consideration. The two‐stage randomized trial design allows researchers to test patient's preference and selection effects, in addition to the treatment effect. The current methodology for the two‐stage design is limited to continuous and binary outcomes; this article extends the model to include count outcomes. The test statistics for preference, selection, and treatment effects are derived. Closed‐form sample size formulae are presented for each effect. Simulations are presented to demonstrate the properties of the unstratified and stratified designs. Finally, we apply methods to the use of antimicrobials at the end of life to demonstrate the applicability of the methods.