Powering population health research: Considerations for plausible and actionable effect sizes. (June 2021)
- Record Type:
- Journal Article
- Title:
- Powering population health research: Considerations for plausible and actionable effect sizes. (June 2021)
- Main Title:
- Powering population health research: Considerations for plausible and actionable effect sizes
- Authors:
- Matthay, Ellicott C.
Hagan, Erin
Gottlieb, Laura M.
Tan, May Lynn
Vlahov, David
Adler, Nancy
Glymour, M. Maria - Abstract:
- Abstract: Evidence for Action (E4A), a signature program of the Robert Wood Johnson Foundation, funds investigator-initiated research on the impacts of social programs and policies on population health and health inequities. Across thousands of letters of intent and full proposals E4A has received since 2015, one of the most common methodological challenges faced by applicants is selecting realistic effect sizes to inform calculations of power, sample size, and minimum detectable effect (MDE). E4A prioritizes health studies that are both (1) adequately powered to detect effect sizes that may reasonably be expected for the given intervention and (2) likely to achieve intervention effects sizes that, if demonstrated, correspond to actionable evidence for population health stakeholders. However, little guidance exists to inform the selection of effect sizes for population health research proposals. We draw on examples of five rigorously evaluated population health interventions. These examples illustrate considerations for selecting realistic and actionable effect sizes as inputs to calculations of power, sample size and MDE for research proposals to study population health interventions. We show that plausible effects sizes for population health interventions may be smaller than commonly cited guidelines suggest. Effect sizes achieved with population health interventions depend on the characteristics of the intervention, the target population, and the outcomes studied.Abstract: Evidence for Action (E4A), a signature program of the Robert Wood Johnson Foundation, funds investigator-initiated research on the impacts of social programs and policies on population health and health inequities. Across thousands of letters of intent and full proposals E4A has received since 2015, one of the most common methodological challenges faced by applicants is selecting realistic effect sizes to inform calculations of power, sample size, and minimum detectable effect (MDE). E4A prioritizes health studies that are both (1) adequately powered to detect effect sizes that may reasonably be expected for the given intervention and (2) likely to achieve intervention effects sizes that, if demonstrated, correspond to actionable evidence for population health stakeholders. However, little guidance exists to inform the selection of effect sizes for population health research proposals. We draw on examples of five rigorously evaluated population health interventions. These examples illustrate considerations for selecting realistic and actionable effect sizes as inputs to calculations of power, sample size and MDE for research proposals to study population health interventions. We show that plausible effects sizes for population health interventions may be smaller than commonly cited guidelines suggest. Effect sizes achieved with population health interventions depend on the characteristics of the intervention, the target population, and the outcomes studied. Population health impact depends on the proportion of the population receiving the intervention. When adequately powered, even studies of interventions with small effect sizes can offer valuable evidence to inform population health if such interventions can be implemented broadly. Demonstrating the effectiveness of such interventions, however, requires large sample sizes. Highlights: Effect sizes achievable with population health interventions are likely small. Effect sizes depend on intervention characteristics, mechanisms of effect, target population, and outcome measures. Small individual-level effects can translate to large population health effects. Adequate power will usually require large sample sizes. Power calculations can use the smallest effect size that would justify intervention adoption. … (more)
- Is Part Of:
- SSM - population health. Volume 14(2021)
- Journal:
- SSM - population health
- Issue:
- Volume 14(2021)
- Issue Display:
- Volume 14, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 14
- Issue:
- 2021
- Issue Sort Value:
- 2021-0014-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-06
- Subjects:
- Statistical power -- Effect size -- Sample size -- Social intervention -- Population health -- Health equity
Social medicine -- Periodicals
Medical anthropology -- Periodicals
Public health -- Periodicals
Psychology -- Periodicals
Medicine -- Periodicals
362.105 - Journal URLs:
- http://www.sciencedirect.com/science/journal/23528273 ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.ssmph.2021.100789 ↗
- Languages:
- English
- ISSNs:
- 2352-8273
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 17285.xml