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Outcomes from studies assessing exposure often use multiple measurements. Inprevious work, using a model first proposed by Buonoccorsi (1991), we showedthat combining direct (e.g. biomarkers) and indirect (e.g. self-report)measurements provides a more accurate picture of true exposure than estimatesobtained when using a single type of measurement. In this article, we propose avaluable tool for efficient design of studies that include both direct andindirect measurements of a relevant outcome. Based on data from a pilot orpreliminary study, the tool, which is available online as a shiny app\citep{shinyR}, can be used to compute: (1) the sample size required for astatistical power analysis, while optimizing the percent of participants whoshould provide direct measures of exposure (biomarkers) in addition to theindirect (self-report) measures provided by all participants; (2) the idealnumber of replicates; and (3) the allocation of resources to intervention andcontrol arms. In addition we show how to examine the sensitivity of results tounderlying assumptions. We illustrate our analysis using studies of tobaccosmoke exposure and nutrition. In these examples, a near-optimal allocation ofthe resources can be found even if the assumptions are not precise.

Efficient Study Design with Multiple Measurement Instruments