Relative risk reduction is useful metric to standardize effect size for public heath interventions for translational research

J Clin Epidemiol. 2015 Mar;68(3):317-23. doi: 10.1016/j.jclinepi.2014.11.013. Epub 2014 Dec 18.

Abstract

Objectives: Heterogeneity of effect measures in intervention studies undermines the use of evidence to inform policy. Our objective was to develop a comprehensive algorithm to convert all types of effect measures to one standard metric, relative risk reduction (RRR).

Study design and setting: This work was conducted to facilitate synthesis of published intervention effects for our epidemic modeling of the health impact of human immunodeficiency virus [HIV testing and counseling (HTC)]. We designed and implemented an algorithm to transform varied effect measures to RRR, representing the proportionate reduction in undesirable outcomes.

Results: Our extraction of 55 HTC studies identified 473 effect measures representing unique combinations of intervention-outcome-population characteristics, using five outcome metrics: pre-post proportion (70.6%), odds ratio (14.0%), mean difference (10.2%), risk ratio (4.4%), and RRR (0.9%). Outcomes were expressed as both desirable (29.5%, eg, consistent condom use) and undesirable (70.5%, eg, inconsistent condom use). Using four examples, we demonstrate our algorithm for converting varied effect measures to RRR and provide the conceptual basis for advantages of RRR over other metrics.

Conclusion: Our review of the literature suggests that RRR, an easily understood and useful metric to convey risk reduction associated with an intervention, is underused by original and review studies.

Keywords: Decision making; Effect measures; Hazard ratio; Odds ratio; Relative risk reduction; Risk factor; Risk ratio.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Clinical Trials as Topic
  • HIV / pathogenicity
  • HIV Infections / epidemiology
  • HIV Infections / prevention & control*
  • HIV Infections / transmission
  • Humans
  • Meta-Analysis as Topic
  • Public Health*
  • Risk Reduction Behavior*
  • Translational Medical Research*