Published Dec 2009
Cox E, Martin BC, Van Staa T, Garbe E, Siebert U, Johnson ML. Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of non-randomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force–part II. Value Health. 2009;12(8):1053-1061.
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
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
Conclusions: Design of nonrandomized studies of comparative effectiveness
face several daunting issues, including measurement of exposure and
outcome challenged by misclassification and confounding. Use of causal
diagrams and restriction are two techniques that can improve the theoretical
basis for analyzing treatment effects in study populations of more
homogeneity, with reduced loss of generalizability.
Keywords: comparative effectiveness, epidemiology, nonrandomized studies, research design, secondary databases.
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- Good Research Practices for Comparative Effectiveness Research: Analytic Methods To Improve Causal Inference From Non-randomized Studies of Treatment Effects Using Secondary Databases: Report 3
- Good Research Practices for Comparative Effectiveness Research: Defining, Reporting and Interpreting Non-randomized Studies Of Treatment Effects Using Secondary Databases: Report 1