Causal Analyses of Nested Case-Control Studies for Comparative Effectiveness Research [Methods Study], Washington, 2018-2021 (ICPSR 39715)

Version Date: Mar 11, 2026

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Principal Investigator(s):
Goodarz Danaei, Harvard University

https://doi.org/10.3886/ICPSR39715.v1

Version V1

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Summary

A randomized controlled trial, or RCT, is the best way to compare how well different treatments work to improve patients' health. In RCTs, researchers assign patients to treatment groups by chance. But RCTs aren't always an option due to high costs or ethical concerns. In these cases, researchers use other types of study designs such as

  • Cohort studies, which look at patients' data over time to see how a treatment affects the risk of a certain health event, such as a heart attack
  • Case-control studies, which compare data from patients who did and didn't have a certain health event

These designs often use data from health records to compare treatment results. In these studies, researchers use statistical methods to make results more like results from RCTs. Current methods work well for cohort studies but not for case-control studies.

In this study, the research team created and tested new methods and a guide to analyze case-control studies so that results would be more like results from an RCT.

Citation

Danaei, Goodarz. Causal Analyses of Nested Case-Control Studies for Comparative Effectiveness Research [Methods Study], Washington, 2018-2021. Inter-university Consortium for Political and Social Research [distributor], 2026-03-11. https://doi.org/10.3886/ICPSR39715.v1

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Funding

Patient-Centered Outcomes Research Institute (PCORI) (ME-1609-36748)

Subject Terms

Geographic Coverage

Distributor(s)

Inter-university Consortium for Political and Social Research
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Time Period(s)

2018 -- 2021
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Study Purpose

To reduce bias in comparative effectiveness research in case-control studies by developing causal methods that emulate the design and analysis of a hypothetical RCT, or a target trial

Universe

Investigators and data analysts identified by the University of Washington, Kaiser Permanente Washington, and Harvard T. H. Chan School of Public Health

Data Source

2 sets of semistructured interviews with 20 investigators and data analysts identified by the University of Washington, Kaiser Permanente Washington, and Harvard T. H. Chan School of Public Health EMR data from Kaiser Permanente Washington enrollees from January 1, 1993, through December 31, 2014

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Original Release Date

2026-03-11

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Notes

  • The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.

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