ArticleAmerican journal of epidemiology2021
On the Causal Interpretation of Rate-Change Methods: The Prior Event Rate Ratio and Rate Difference.
Article in American journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Periprosthetic joint infection as failure of containment: A causal and probabilistic framework.Journal of orthopaedic translation · 2026Article
- An evolved interpretation of Austin Bradford Hill's causal viewpoints and their influence on epidemiologic methods.American journal of epidemiology · 2025Article
- Foundational model aided automatic high-throughput drug screening using self-controlled cohort study.medRxiv : the preprint server for health sciences · 2024Article
- Learning about treatment effects in a new target population under transportability assumptions for relative effect measures.European journal of epidemiology · 2024Article
- Association of Implementation of Postoverdose Outreach Programs With Subsequent Opioid Overdose Deaths Among Massachusetts Municipalities.JAMA psychiatry · 2023Article
- Economic Assessment of High-Dose Versus Adjuvanted Influenza Vaccine: An Evaluation of Hospitalization Costs Based on a Cohort Study.Vaccines · 2021Article
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Authors and funding
5 authors.
Funding
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Abstract
A growing number of studies use data before and after treatment initiation in groups exposed to different treatment strategies to estimate "causal effects" using a ratio measure called the prior event rate ratio (PERR). Here, we offer a causal interpretation for PERR and its additive scale analog, the prior event rate difference (PERD). We show that causal interpretation of these measures requires untestable rate-change assumptions about the relationship between 1) the change of the counterfactual rate before and after treatment initiation in the treated group under hypothetical intervention to implement the control strategy; and 2) the change of the factual rate before and after treatment initiation in the control group. The rate-change assumption is on the multiplicative scale for PERR but on the additive scale for PERD; the 2 assumptions hold simultaneously under testable, but unlikely, conditions. Even if investigators can pick the most appropriate scale, the relevant rate-change assumption might not hold exactly, so we describe sensitivity analysis methods to examine how assumption violations of different magnitudes would affect study results. We illustrate the methods using data from a published study of proton pump inhibitors and pneumonia.
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