On a Class of Optimal Type Covariate Adjusted Response Adaptive Allocations for Normal Treatment Responses

Bhattacharya, Rahul; Bandyopadhyay, Uttam

doi:10.17713/ajs.v44i4.69

Cited by 1 publication

(3 citation statements)

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“…This could be important in cases where treatment differentials exist across covariate profiles. 6,[14][15][16] Not only are these methods statistically innovative, but they are clinically and ethically beneficial as well. We aim to minimize the number of subjects assigned to the inferior treatment group and minimize the total expected response.…”

Section: Discussionmentioning

confidence: 99%

“…Another choice for the allocation equation is the effect-size mapping approach defined by Bandyopadhyay and Bhattacharya's optimal design, 6 which aims to maximize or minimize the expected treatment responses, depending on whether a low or high response is best. While they propose several ethical metrics to optimize depending on the goal of the research, we utilized the following allocation equation:…”

Section: Response-adaptive Methodsmentioning

confidence: 99%

“…In Figure 1, the left hand side are the results when using the moment-based allocation weight equation (Equation (1)), while the right hand side of the figure are the results when using the effectsize mapping allocation weight equation (Equation 2). 1,6 In general, the moment-based allocation equation is more conservative, maintaining close to equal allocation for much of the trial, regardless of scenario, while the effect-size mapping allocation equation is more sensitive to the signal detected in each scenario. For comparison, the first row of Figure 1 depicts the frequentist RA approach with a hard lead-in, while each subsequent row in Figure 1 represents a different DIP formulation.…”

Section: Comparing Dip Methodsmentioning

confidence: 99%

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A natural lead‐in approach to response‐adaptive allocation for continuous outcomes

Donahue

Sabo

2021

Pharmaceutical Statistics

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Response-adaptive Methodsmentioning

confidence: 99%

Section: Comparing Dip Methodsmentioning

confidence: 99%

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