Making the Most of Your Research Budget: Efficiency of a Three-Method Measurement Design With Planned Missing Data

Abstract

Planned missing data (PMD) designs are an elegant way to incorporate expensive gold standard methods (e.g., biomarker) and cheaper but systematically biased methods (e.g., questionnaires) in research designs while ensuring high statistical power and low research costs. This article outlines a PMD design with one expensive gold standard and two cheap but biased methods (three-method measurement [3-MM] design). The cost effectiveness of different 3-MM-PMD designs is investigated and compared with the cost effectiveness of corresponding same-price two-method measurement designs using a simulation study. The results underline that PMD designs yield higher statistical power compared with complete data designs in a wide variety of conditions. Adding a second cheap method to the measurement model (i.e., using a 3-MM-PMD design) can increase the statistical power of the research design even further while keeping costs constant, when the additional measure is inexpensive, shares only small amounts of bias variance with the initial cheap measure, and when the gold standard measure is highly expensive compared with the cheap measures. Recommendations as well as a computer program for finding the optimal research design are provided.