JSM 2023: Comparing Normal and Binary D-Optimal Design of Experiments by Statistical Power
Abstract
In many Department of Defense (DoD) Test and Evaluation (T and E) applications, binary response variables are unavoidable. Many have considered D-optimal design of experiments (DOEs) for generalized linear models (GLMs). However, little consideration has been given to assessing how these new designs perform in terms of statistical power for a given hypothesis test. Monte Carlo simulations and exact power calculations suggest that D-optimal designs generally yield higher power than binary D-optimal designs, despite using logistic regression in the analysis after the data have been collected. Results from using statistical power to compare designs contradict traditional DOE comparisons which employ D-efficiency ratios and fractional design space (FDS) plots. Power calculations suggest that practitioners that are primarily interested in the resulting statistical power of a design should use normal D-optimal designs over binary D-optimal designs when logistic regression is to be used in the data analysis after data collection.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2023
- Accession Number
- AD1223453
Entities
People
- Addison D. Adams
- Rebecca M. Medlin
Organizations
- Institute for Defense Analyses