Simulation study of Bayesian adaptive design conditions¶

Element · All elements
Evidence · 1 claim (1 for) · 1 study (1 theoretical), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article includes a simulation study built around a randomized experiment of students in an online course as a motivating example. Its purpose is to identify the conditions under which the Bayesian adaptive design can produce better inference and ultimately smaller trials, and it reports conditions with more than a 90 percent chance of superior inference using less than one-third the sample size.

Design Implications¶

Context¶

Requirements¶

• A motivating randomized experiment to parameterize the simulations

Constraints¶

• Findings are simulation-based and conditional on the circumstances the authors describe

Target Learners¶

• Students in online courses

Target Learning Goals¶

• Evaluating which interventions work for which learners

Claims¶

• The adaptive trial design reallocates more study subjects to more promising treatment arms as evidence accumulates [+W]

Related Elements¶

Examples¶

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Key Sources¶

• 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