Robust Designs for Prospective Randomized Trials Surveying Sensitive Topics

TL;DR

This paper explores how misreporting in self-reported sensitive-topic trials affects statistical power and proposes a new method for sample size calculation considering worst-case misreporting scenarios.

Contribution

It introduces a model linking reporting behaviors to bias and variance, and proposes a novel sample size determination procedure accounting for misreporting.

Findings

Bias and variance are characterized by reporting and response class distributions.

A new sample size calculation method is developed for worst-case misreporting.

Application to a trial on sexual violence prevention demonstrates practical relevance.

Abstract

We consider the problem of designing a prospective randomized trial in which the outcome data will be self-reported, and will involve sensitive topics. Our interest is in misreporting behavior, and how respondents' tendency to under- or overreport a binary outcome might affect the power of the experiment. We model the problem by assuming each individual in our study is a member of one "reporting class": a truth-teller, underreporter, overreporter, or false-teller. We show that the joint distribution of reporting classes and "response classes" (characterizing individuals' response to the treatment) will exactly define the bias and variance of the causal estimate in our experiment. Then, we propose a novel procedure for deriving sample sizes under the worst-case power corresponding to a given level of misreporting. Our problem is motivated by prior experience implementing a randomized controlled trial of a sexual violence prevention program among adolescent girls in Nairobi, Kenya.