Efficient Study Design with Multiple Measurement Instruments

TL;DR

This paper introduces an online tool for designing studies with multiple measurement types, optimizing sample size, resource allocation, and replicates based on preliminary data to improve exposure assessment accuracy.

Contribution

It presents a novel, practical tool for efficient study design incorporating both direct and indirect measurements, with sensitivity analysis capabilities.

Findings

The tool computes optimal sample sizes and resource allocation.

Near-optimal resource allocation is achievable even with assumption uncertainties.

Applications demonstrated with tobacco smoke and nutrition studies.

Abstract

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.