Reverse causation biases weighted cumulative exposure model estimates, but can be investigated in sensitivity analyses

Nirit Agay, Rachel Dankner, Havi Murad, Liraz Olmer, Laurence S. Freedman

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Objectives: To examine the effects of reverse causation on estimates from the weighted cumulative exposure (WCE) model that is used in pharmacoepidemiology to explore drug-health outcome associations, and to identify sensitivity analyses for revealing such effects. Study Design and Setting: 314,099 patients with diabetes under Clalit Health Services, Israel, were followed over 2002–2012. The association between metformin and pancreatic cancer (PC) was explored using a WCE model within the framework of discrete-time Cox regression. We used computer simulations to explore the effects of reverse causation on estimates of a WCE model and to examine sensitivity analyses for revealing and adjusting for reverse causation. We then applied those sensitivity analyses to our data. Results: Simulation demonstrated bias in the weighted cumulative exposure model and showed that sensitivity analysis could reveal and adjust for these biases. In our data, a positive association was observed (hazard ratio (HR) = 3.24, 95% confidence interval (CI): 2.24–4.73) with metformin exposure in the previous 2 years. After applying sensitivity analysis, assuming reverse causation operated up to 4 years before cancer diagnosis, the association between metformin and PC was no longer apparent. Conclusion: Reverse causation can cause substantial bias in the WCE model. When suspected, sensitivity analyses based on causal analysis are advocated.

Original languageEnglish
Pages (from-to)46-52
Number of pages7
JournalJournal of Clinical Epidemiology
Volume161
DOIs
StatePublished - Sep 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Inc.

Keywords

  • Causal analysis
  • Metformin
  • Pancreatic neoplasms
  • Pharamcoepidemiology
  • Protopathic bias
  • Reverse causation

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