The adaptive trial design reallocates more study subjects to more promising treatment arms as evidence accumulates¶

Claim · All claims
Evidence · 1 study · theoretical r2 · q2
Currency · newest study 2015 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Over the course of a trial, more study subjects are allocated to treatment arms that are more promising for the specific subgroup from which each subject comes. → Mariel McKenzie Finucane 2015

Evidence¶

Mariel McKenzie Finucane 2015¶

Mariel McKenzie Finucane, Ignacio Martinez, Scott Cody. (2015). What Works for Whom? A Bayesian Approach to Channeling Big Data Streams for Policy Analysis. Working Paper 40, Mathematica Policy Research. https://www.mathematica.org/publications/what-works-for-whom-a-bayesian-approach-to-channeling-big-data-streams-for-policy-analysis

q2 · i? · theoretical · r2

Description of the adaptive allocation mechanism of the proposed Bayesian design, stated in the paper's overview of the method. The mechanism responds to accumulating evidence and is subgroup-specific; no effect size or trial statistic is printed in the available text.

"The trial design adapts to accumulating evidence: over the course of a trial, more study subjects are allocated to treatment arms that are more promising, given the specific subgroup from which each subject comes"

Discussion¶

Related Claims¶

• Bayesian adaptive design benefits study subjects by allocating them to more effective treatment arms — possibly the same claim (merge candidate)
• Bayesian adaptive design benefits evaluators by allowing smaller and more informative studies — related
• A Bayesian adaptive design can yield superior inference to a standard design using less than one third the sample size under identified conditions — related