Good Research Practices for Comparative Effectiveness Research: Analytic Methods to Improve Causal Inference from Nonrandomized Studies of Treatment Effects Using Secondary Data Sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—Part III

Johnson, Michael L.; Crown, William; Martin, Bradley C.; Dormuth, Colin R.; Siebert, Uwe

doi:10.1111/j.1524-4733.2009.00602.x

Cited by 285Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyMeta-analysis and systematic reviews

Good Research Practices for Comparative Effectiveness Research: Analytic Methods to Improve Causal Inference from Nonrandomized Studies of Treatment Effects Using Secondary Data Sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—Part III

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Johnson, Michael L., Crown, William, Martin, Bradley C., Dormuth, Colin R., Siebert, Uwe·2009·Value in Health

DOI 10.1111/j.1524-4733.2009.00602.x

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Abstract

Objectives Most contemporary epidemiologic studies require complex analytical methods to adjust for bias and confounding. New methods are constantly being developed, and older more established methods are yet appropriate. Careful application of statistical analysis techniques can improve causal inference of comparative treatment effects from nonrandomized studies using secondary databases. A Task Force was formed to offer a review of the more recent developments in statistical control of confounding. Methods The Task Force was commissioned and a chair was selected by the ISPOR Board of Directors in October 2007. This Report, the third in this issue of the journal, addressed methods to improve causal inference of treatment effects for nonrandomized studies. Results The Task Force Report recommends general analytic techniques and specific best practices where consensus is reached including: use of stratification analysis before multivariable modeling, multivariable regression including model performance and diagnostic testing, propensity scoring, instrumental variable, and structural modeling techniques including marginal structural models, where appropriate for secondary data. Sensitivity analyses and discussion of extent of residual confounding are discussed. Conclusions Valid findings of causal therapeutic benefits can be produced from nonrandomized studies using an array of state-of-the-art analytic techniques. Improving the quality and uniformity of these studies will impr…

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Adjustment for time-varying confounding (confounders that simultaneously act as intermediate steps in the pathway between intervention and outcome) requires special methods such as marginal structural analysis or g-estimation.
“Adjustment for time-varying confounding (i.e., confounders that simultaneously act as intermediate steps in the pathway between intervention and outcome) require special methods such as marginal structural analysis or g-estimation [38,54].”
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Is Propensity Score Analysis a Valid Surrogate of Randomization for the Avoidance of Allocation Bias?
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The relative impact of brief treatment versus brief intervention in primary health‐care screening programs for substance use disorders
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Does statin-treated hyperlipidemia affect rotator cuff healing or muscle fatty infiltration after rotator cuff repair?
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Good Research Practices for the Analysis of Non-randomized Studies of Treatment Effects Using Secondary Databases 1 III. GOOD RESEARCH PRACTICES FOR THE ANALYSIS OF NON-RANDOMIZED STUDIES OF TREATMENT EFFECTS USING SECONDARY DATABASES:
Ml, J., Crown, W., Martin, B., Dormuth, C., & Siebert, U. (2015)
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
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Self-citation
Practices for Retrospective Database Analysis Task Force
(2014)
Good Research Practices for Comparative Effectiveness Research: Defining, Reporting and Interpreting Nonrandomized Studies of Treatment Effects Using Secondary Data Sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—Part I
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Comparative effectiveness research methodology using secondary data: A starting user’s guide
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Authors: Johnson, M. L., Crown, W., Martin, B. C., Dormuth, C. R., & Siebert, U.
Affiliations: University of Houston (US)
University of Arkansas for Medical Sciences (US)
University of British Columbia (CA)
Harvard University (US)
Corresponding author: Johnson, Michael L.
ORCID: Johnson, Michael L.
Crown, William
Martin, Bradley C.
Dormuth, Colin R.
Siebert, Uwe
Venue: Value in Health
Publisher: Elsevier BV
Published: 2009-11
Volume: 12
Issue: 8
Pages: 1062-1073
ISSN: 1098-3015 (ISSN-L)
DOI: 10.1111/j.1524-4733.2009.00602.x
OpenAlex: W2153012086
Cited by: 285
References: 77
Citations by year: 2026: 2 · 2025: 6 · 2024: 10 · 2023: 10 · 2022: 18 · 2021: 14 · 2020: 25 · 2019: 19 · 2018: 11 · 2017: 30 · 2016: 30 · 2015: 17 · 2014: 19 · 2013: 24 · 2012: 16
FWCI: 13.87
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 1353 d)
Locations: Value in Health (publishedVersion)
PubMed (publishedVersion)
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, causal inference, non-randomized studies, secondary data sources, confounding adjustment, propensity scoring, instrumental variables, marginal structural models, structural equation modeling, stratified analysis, multivariable regression, residual confounding, sensitivity analysis, good scientific practice
MeSH terms: Data Interpretation, Statistical, Decision Making, Epidemiologic Methods, Health Policy, Humans, Regression Analysis, Retrospective Studies, ROC Curve, Selection Bias, Causality, Confounding Factors, Epidemiologic, Multivariate Analysis, Databases, Factual, Outcome Assessment, Health Care, Economics, Pharmaceutical, Internationality, Comparative Effectiveness Research, Drug-Related Side Effects and Adverse Reactions

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Good Research Practices for Comparative Effectiveness Research: Analytic Methods to Improve Causal Inference from Nonrandomized Studies of Treatment Effects Using Secondary Data Sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—Part III

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