Evidence map›Paper›PMID 28590865›Full record

ArticleAmerican journal of public health2017

Evaluating Public Health Interventions: 6. Modeling Ratios or Differences? Let the Data Tell Us.

Donna Spiegelman, Tyler J VanderWeele

Abstract readEvaluation Study
In one paragraph

Article in American journal of public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Treatment Effect Measures for Culture Conversion Endpoints in Phase IIb Tuberculosis Treatment Trials.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021
    Pooled it
  2. Article
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  4. Review
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  16. Article
  17. The Interaction Continuum.Epidemiology (Cambridge, Mass.) · 2019
    Review
  18. Spiegelman and Zhou Respond.American journal of public health · 2019
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Donna SpiegelmanDonna Spiegelman is with the departments of Epidemiology, Biostatistics, Nutrition, and Global Health, Harvard T. H. Chan School of Public Health, Boston, MA. Tyler J. VanderWeele is with the Department of Epidemiology, Harvard T. H. Chan School of Public Health.
Tyler J VanderWeeleDonna Spiegelman is with the departments of Epidemiology, Biostatistics, Nutrition, and Global Health, Harvard T. H. Chan School of Public Health, Boston, MA. Tyler J. VanderWeele is with the Department of Epidemiology, Harvard T. H. Chan School of Public Health.

Funding

Comprehensive Translational Science Analytics Tools for the Global Health AgendaDP1ES025459 · NIEHS · YALE UNIVERSITY · PI SPIEGELMAN, DONNA L · 2014 to 2018
$4.1M
New Methods for the design and evaluation of large HIV prevention interventionsR01AI112339 · NIAID · YALE UNIVERSITY · PI SPIEGELMAN, DONNA L · 2014 to 2017
$2.7M
Theory and methods for interactionR56ES017876 · NIEHS · HARVARD SCHOOL OF PUBLIC HEALTH · PI TCHETGEN TCHETGEN, ERIC JOEL, VANDERWEELE, TYLER · 2016 to 2016
$150k
NIAID NIH HHS R01 AI112339NIEHS NIH HHS DP1 ES025459NIEHS NIH HHS R56 ES017876
6 · The paper itself

Abstract

We provide an overview of the relative merits of ratio measures (relative risks, risk ratios, and rate ratios) compared with difference measures (risk and rate differences). We discuss evidence that the multiplicative model often fits the data well, so that rarely are interactions with other risk factors for the outcome observed when one uses a logistic, relative risk, or Cox regression model to estimate the intervention effect. As a consequence, additive models, which estimate the risk or rate difference, will often exhibit interactions. Under these circumstances, absolute measures of effect, such as years of life lost, disability- or quality-adjusted years of life lost, and number needed to treat, will not be externally generalizable to populations other than those with similar risk factor distributions as the population in which the intervention effect was estimated. Nevertheless, these absolute measures are often of the greatest importance in public health decision-making. When studies of high-risk study populations are used to more efficiently estimate effects, these populations will not be representative of the general population's risk factor distribution. The relative homogeneity of ratio versus absolute measures will thus have important implications for the generalizability of results across populations.

Indexed as

Data Interpretation, StatisticalOdds RatioPublic HealthRiskHumansModels, Theoretical

Identifiers

PMID28590865
PMCPMC5463222

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.