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

The DOI matches its Crossref record; 75 of the 77 references it cites were found in a registry or an index.

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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Research works printed but not linked: 2

  1. Berger, 2009, Good research practices for comparative effectiveness research: defining, reporting and interpreting non-randomized studies of treatment effects using secondary data sources. ISPOR TF Report 2009—Part I, Value Health
  2. Montaquila JM., Ponikowski CH. An evaluation of alternative imputation methods. Proceedings of the Section on Survey Research Methods, American Statistical Association, 1995.

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Metadata

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
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
Licence
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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