Good Research Practices for Comparative Effectiveness Research: Approaches to Mitigate Bias and Confounding in the Design of Nonrandomized Studies of Treatment Effects Using Secondary Data Sources: The International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report—Part II
Open accessAbstract
Objectives The goal of comparative effectiveness analysis is to examine the relationship between two variables, treatment, or exposure and effectiveness or outcome. Unlike data obtained through randomized controlled trials, researchers face greater challenges with causal inference with observational studies. Recognizing these challenges, a task force was formed to develop a guidance document on methodological approaches to addresses these biases. Methods The task force was commissioned and a Chair was selected by the International Society for Pharmacoeconomics and Outcomes Research Board of Directors in October 2007. This report, the second of three reported in this issue of the Journal, discusses the inherent biases when using secondary data sources for comparative effectiveness analysis and provides methodological recommendations to help mitigate these biases. Results The task force report provides recommendations and tools for researchers to mitigate threats to validity from bias and confounding in measurement of exposure and outcome. Recommendations on design of study included: the need for data analysis plan with causal diagrams; detailed attention to classification bias in definition of exposure and clinical outcome; careful and appropriate use of restriction; extreme care to identify and control for confounding factors, including time-dependent confounding. Conclusions Design of nonrandomized studies of comparative effectiveness face several daunting issues, including…
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Efficacy derived from randomized clinical trials may have to be adjusted for compliance to reflect real-world effectiveness.
“Efficacy derived from randomized clinical trials may have to be adjusted for compliance to reflect real-world effectiveness [53].”
From State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3 (2012) · cites this paper as [53]
When results from observational studies are used in a model, causal graphs can be used to explicitly state causal assumptions.
“When results from observational studies are used in the model, causal graphs can be used to explicitly state causal assumptions [53].”
From State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3 (2012) · cites this paper as [53]
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Causal Diagrams for Epidemiologic Research
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Johnson, M. L., Crown, W., Martin, B. C., Dormuth, C. R., & Siebert, U. (2009) · Value in Health · doi:10.1111/j.1524-4733.2009.00602.x · Cited by 285 · see citing works
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State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3
Siebert, U., Alagoz, O., Bayoumi, A. M., Jahn, B., Owens, D. K., Cohen, D. J., & Kuntz, K. M. (2012) · Value in Health · doi:10.1016/j.jval.2012.06.014 · Cited by 495 · see citing works
Self-citationState-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–3
Siebert, U., Alagoz, O., Bayoumi, A. M., Jahn, B., Owens, D. K., Cohen, D. J., & Kuntz, K. M. (2012) · Medical Decision Making · doi:10.1177/0272989x12455463 · Cited by 348 · see citing works
Self-citationJohnson, M. L., Crown, W., Martin, B. C., Dormuth, C. R., & Siebert, U. (2009) · Value in Health · doi:10.1111/j.1524-4733.2009.00602.x · Cited by 285 · see citing works
Self-citationSuissa, S., Moodie, E. E. M., & Dell'Aniello, S. (2017) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4107 · Cited by 239 · see citing works
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Does the encounter type matter when defining diabetes complications in electronic health records?
Hong, D., Shen, Y., Monnette, A., Liu, S., Shao, H., Nauman, E., Price-Haywood, E., Hu, G., & Shi, L. (2020) · Medical Care · doi:10.1097/mlr.0000000000001297 · Cited by 4 · see citing works
- Authors
- Cox, E., Martin, B. C., Van Staa, T., Garbe, E., Siebert, U., & Johnson, M. L.
- Affiliations
- Express Scripts Holding Company (United States) (US)
- University of Arkansas for Medical Sciences (US)
- Leibniz Institute for Prevention Research and Epidemiology - BIPS (DE)
- Harvard University (US)
- University of Houston (US)
- Venue
- Value in Health
- Publisher
- Elsevier BV
- Published
- 2009-11
- Volume
- 12
- Issue
- 8
- Pages
- 1053-1061
- ISSN
- 1098-3015 (ISSN-L)
- OpenAlex
- W2121797973
- Cited by
- 307
- References
- 2
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 11 · 2023: 7 · 2022: 25 · 2021: 19 · 2020: 25 · 2019: 26 · 2018: 19 · 2017: 24 · 2016: 23 · 2015: 24 · 2014: 22 · 2013: 22 · 2012: 17
- FWCI
- 13.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Value in Health (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- comparative effectiveness research, observational studies, confounding, bias, secondary data sources, causal graphs, restriction, misclassification bias, time-varying confounding, research design, generalizability, treatment effects
- MeSH terms
- Data Collection, Data Interpretation, Statistical, Decision Making, Health Policy, Humans, Retrospective Studies, United States, Selection Bias, Confounding Factors, Epidemiologic, Databases, Factual, Outcome Assessment, Health Care, Economics, Pharmaceutical, Observation, Advisory Committees, Internationality, Comparative Effectiveness Research, Drug-Related Side Effects and Adverse Reactions
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