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A natural lead‐in approach to response‐adaptive allocation for continuous outcomes
Author(s) -
Donahue Erin,
Sabo Roy T.
Publication year - 2021
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.2094
Subject(s) - sample size determination , frequentist inference , bayesian probability , estimator , econometrics , prior probability , adaptation (eye) , skew , statistics , computer science , mathematics , bayesian inference , psychology , telecommunications , neuroscience
Abstract Response‐adaptive (RA) allocation designs can skew the allocation of incoming subjects toward the better performing treatment group based on the previously accrued responses. While unstable estimators and increased variability can adversely affect adaptation in early trial stages, Bayesian methods can be implemented with decreasingly informative priors (DIP) to overcome these difficulties. DIPs have been previously used for binary outcomes to constrain adaptation early in the trial, yet gradually increase adaptation as subjects accrue. We extend the DIP approach to RA designs for continuous outcomes, primarily in the normal conjugate family by functionalizing the prior effective sample size to equal the unobserved sample size. We compare this effective sample size DIP approach to other DIP formulations. Further, we considered various allocation equations and assessed their behavior utilizing DIPs. Simulated clinical trials comparing the behavior of these approaches with traditional Frequentist and Bayesian RA as well as balanced designs show that the natural lead‐in approaches maintain improved treatment with lower variability and greater power.