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CLINICAL HETEROGENEITY IN SYSTEMATIC REVIEWS AND HEALTH TECHNOLOGY ASSESSMENTS: SYNTHESIS OF GUIDANCE DOCUMENTS AND THE LITERATURE

Published online by Cambridge University Press:  23 January 2012

Gerald Gartlehner ,
Suzanne L. West ,
Alyssa J. Mansfield ,
Charles Poole ,
Elizabeth Tant ,
Linda J. Lux  and
Kathleen N. Lohr
Gerald Gartlehner
Affiliation:
Danube University, Krems; RTI Internationalgerald.gartlehner@donau-uni.ac.at
Suzanne L. West
Affiliation:
RTI International
Alyssa J. Mansfield
Affiliation:
RTI International
Charles Poole
Affiliation:
University of North Carolina at Chapel Hill
Elizabeth Tant
Affiliation:
RTI International
Linda J. Lux
Affiliation:
RTI International
Kathleen N. Lohr
Affiliation:
RTI International
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Abstract

Objectives: The aim of this study was to synthesize best practices for addressing clinical heterogeneity in systematic reviews and health technology assessments (HTAs).

Methods: We abstracted information from guidance documents and methods manuals made available by international organizations that develop systematic reviews and HTAs. We searched PubMed® to identify studies on clinical heterogeneity and subgroup analysis. Two authors independently abstracted and assessed relevant information.

Results: Methods manuals offer various definitions of clinical heterogeneity. In essence, clinical heterogeneity is considered variability in study population characteristics, interventions, and outcomes across studies. It can lead to effect-measure modification or statistical heterogeneity, which is defined as variability in estimated treatment effects beyond what would be expected by random error alone. Clinical and statistical heterogeneity are closely intertwined but they do not have a one-to-one relationship. The presence of statistical heterogeneity does not necessarily indicate that clinical heterogeneity is the causal factor. Methodological heterogeneity, biases, and random error can also cause statistical heterogeneity, alone or in combination with clinical heterogeneity.

Conclusions: Identifying potential modifiers of treatment effects (i.e., effect-measure modifiers) is important for researchers conducting systematic reviews and HTAs. Recognizing clinical heterogeneity and clarifying its implications helps decision makers to identify patients and patient populations who benefit the most, who benefit the least, and who are at greatest risk of experiencing adverse outcomes from a particular intervention.

Keywords

Clinical heterogeneityComparative effectivenessEffect measure modificationHealth technology assessmentSystematic review
Type
METHODS
Information
International Journal of Technology Assessment in Health Care , Volume 28 , Issue 1 , January 2012 , pp. 36 - 43
Copyright
Copyright © Cambridge University Press 2012

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References

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Supplementary material: File

Gartlehner Supplementary Table

Supplementary Table 1: Summary of common statistical approaches to test for and explore heterogeneity

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