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Activity Number: 408 - Health Policy Statistics Student Paper Awards:
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Health Policy Statistics Section
Abstract #312323
Title: Nothing to See Here? Non-Inferiority Approaches to Parallel Trends and Other Model Assumptions
Author(s): Alyssa Bilinski* and Laura Hatfield
Companies: and Harvard Medical School
Keywords: difference-in-differences; causal inference; non-inferiority; model assumptions

Many causal models make assumptions of "no difference" or "no effect." For example, difference-in-differences (DID) assumes that there is no trend difference between treatment and comparison groups' untreated potential outcomes ("parallel trends"). Tests of these assumptions typically assume a null hypothesis that there is no violation. We argue this approach is incorrect and frequently misleading. We present test reformulations in a non-inferiority framework that rule out violations of model assumptions that exceed a threshold. We then focus on the parallel trends assumption, for which we propose a "one step up" method: 1) reporting treatment effect estimates from a model with a more complex trend difference than is believed to be the case and 2) testing that that the estimated treatment effect falls within a specified distance of the treatment effect from the simpler model. This reduces bias while also considering power, controlling mean-squared error. Our base model also aligns power to detect a treatment effect with power to rule out violations of parallel trends. We apply our approach to 4 data sets used to analyze the Affordable Care Act's dependent coverage mandate.

Authors who are presenting talks have a * after their name.

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