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Activity Number: 99 - Causal Inference with Non-Traditional Designs
Type: Invited
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #300337 Presentation
Title: The Trend-In-Trend Research Design for Causal Inference
Author(s): Ashkan Ertefaie* and Dylan Small and Sean Hennessy and Xinyao Ji and Charles Leonard
Companies: University of Rochester and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
Keywords: unmeasured confounder; causal inference

Cohort studies can be biased by unmeasured confounding. We propose a hybrid ecologic-epidemiologic design called the trend-in-trend design, which requires a strong time trend in exposure, but is unbiased unless there are unmeasured factors affecting outcome for which there are time trends in prevalence that are correlated with time trends in exposure across strata with different exposure trends. Thus, the conditions under which the trend-in-trend study is biased are a subset of those under which a cohort study is biased. The trend-in-trend design first divides the study population into strata based on the cumulative probability of exposure given covariates, which effectively stratifies on time trend in exposure, provided there is a trend. Next, a covariates-free maximum likelihood model estimates the odds ratio (OR) using data on exposure prevalence and outcome frequency within cumulative probability of exposure strata, across multiple periods.The trend-in-trend method may be useful in settings where there is a strong time trend in exposure, such as a newly approved drug or other medical intervention.

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

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