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