Difference between revisions of "Power Calculations in Optimal Design"

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[[Category: Sampling & Power Calculations]]
[[Category: Sampling & Power Calculations]]
[[Category: Reproducible Research]]

Revision as of 14:20, 13 April 2021

Optimal Design is free software designed by University of Michigan. It provides a useful platform on which researchers can visualize the relationship between different elements of the sample size formula when conducting power calculations during the research design stage. This page provides a general overview of and additional resources for Optimal Design.

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Optimal Design creates graphs to visualize trade-offs and the relationships between the various components of power calculations. It can compare, for example, power versus sample size for a given effect or effect size versus sample size for a given desired power. While Optimal Design can aid researcher understanding and decision-making during power calculations, the software has a few shortcomings. First, it is not replicable. Second, it cannot calculate power for an individual-level randomization with binary outcome. Third, it assumes equal mean and variance for treatment and control groups. For a RCT this is generally okay. Fourth, it assumes an equal split of sample size or cluster number between treatment and control groups. For researchers who wish to fix the size of the treatment group, for example, due to budget constraints, and then calculate control group size, this feature is limiting. In general, Stata offers much greater flexibility in power and sampling calculations. DIME Analytics strongly recommends conducting power calculations in Stata and using Optimal Design as a compliment for visualization and general understanding.

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This article is part of the topic Sampling & Power Calculations

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