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