The confounder matrix: A tool to assess confounding bias in systematic reviews of observational studies of etiology

• Authors: Jilie M Petersen, Boston University; Malcolm Barrett, University of Southern California; Katherine A Ahrens, University of Southern Maine; Eleanor J Murray, Boston University; Allison S Bryant, Massachusetts General Hospital; Carol Hogue, Emory University; Sunni L Mumford, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda; Salini Gadupudi, Boston University; Matthew Fox, Boston University; Ludovic Trinquart, Boston University
• Journal Title: RESEARCH SYNTHESIS METHODS
• Volume: Volume 13, Number 2
• Publisher: WILEY | 2022-01-05, Pages 242-254
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1002/jrsm.1544
• Subjects: Health Sciences, Epidemiology; Health Sciences, Public Health; Biology, Biostatistics
• Keywords: Science & Technology; Life Sciences & Biomedicine; Mathematical & Computational Biology; Multidisciplinary Sciences; Science & Technology - Other Topics; bias; epidemiologic confounding factors; evidence-based medicine; interpregnancy interval; meta-analysis; systematic review; INTERPREGNANCY INTERVALS; STRUCTURAL APPROACH; CAUSAL DIAGRAMS; RISK; TIME; METAANALYSES; ADJUSTMENT; ISSUES

Abstract

Systematic reviews and meta-analyses are essential for drawing conclusions regarding etiologic associations between exposures or interventions and health outcomes. Observational studies comprise a substantive source of the evidence base. One major threat to their validity is residual confounding, which may occur when component studies adjust for different sets of confounders, fail to control for important confounders, or have classification errors resulting in only partial control of measured confounders. We present the confounder matrix—an approach for defining and summarizing adequate confounding control in systematic reviews of observational studies and incorporating this assessment into meta-analyses. First, an expert group reaches consensus regarding the core confounders that should be controlled and the best available method for their measurement. Second, a matrix graphically depicts how each component study accounted for each confounder. Third, the assessment of control adequacy informs quantitative synthesis. We illustrate the approach with studies of the association between short interpregnancy intervals and preterm birth. Our findings suggest that uncontrolled confounding, notably by reproductive history and sociodemographics, resulted in exaggerated estimates. Moreover, no studies adequately controlled for all core confounders, so we suspect residual confounding is present, even among studies with better control. The confounder matrix serves as an extension of previously published methodological guidance for observational research synthesis, enabling transparent reporting of confounding control and directly informing meta-analysis so that conclusions are drawn from the best available evidence. Widespread application could raise awareness about gaps across a body of work and allow for more valid inference with respect to confounder control.