text: 493,127,1431284942605066248,366380609,Evan Rosenman,"New paper from me, @rina_friedberg, and @BaiocchiMike: ""Robust Designs for Prospective Randomized Trials Surveying Sensitive Topics"". <LINK> This work emerged out of our experience analyzing a cluster-randomized trial of an empowerment training program deployed to adolescent girls in Nairobi, Kenya. The treatment was intended to reduce the incidence of gender-based violence. When surveying sensitive topics, reporting biases -- e.g. the possibility of underreporting troubling outcomes -- pose a threat to causal inference. We approach the problem under the potential outcomes framework, assuming a binary outcome. We suppose reporting behavior is fixed given the choice of survey and show the joint distribution of ""reporting classes"" (e.g. underreporter, overreporter, truth-teller) and ""response classes"" (e.g. outcome increases, decreases, stays the same) determines the bias exactly. <LINK> Then, we propose a sensitivity model and an optimization procedure to determine the required sample size for achieving a desired power level, given the worst-case configuration of misreporters. <LINK> This is a challenging area! Insights from social scientists + local stakeholders are crucial to design the best survey instruments. Rigorous practices must be followed to preserve comfort and anonymity. Then, statisticians can design procedures to help address residual biases. We hope folks find these results interesting and welcome any feedback!",https://arxiv.org/abs/2108.08944,"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. ","Robust Designs for Prospective Randomized Trials Surveying Sensitive