Literature DB >> 26196684

Is the Risk Difference Really a More Heterogeneous Measure?

Charlie Poole1, Ian Shrier, Tyler J VanderWeele.   

Abstract

There are claims in the literature that the risk difference is a more heterogeneous measure than the odds ratio or risk ratio. These claims are based on surveys of meta-analyses showing that tests reject the null hypothesis of homogeneity more often for the risk difference than for the ratio measures. Discussions of this point have neglected the fact that homogeneity tests can have different levels of statistical power (i.e., different probabilities of rejecting the null when it is false) across different scales. We give hypothetical examples in which there is arguably equal heterogeneity across risk difference and odds ratio measures but in which the risk difference homogeneity test rejects more often, and therefore has higher power, than the odds ratio homogeneity test. These examples suggest that current empirical evidence for the claim that the risk difference is more heterogeneous is not at present satisfactory. Further research could consider other approaches to empirical comparisons of the heterogeneity of the three measures.

Mesh:

Year:  2015        PMID: 26196684     DOI: 10.1097/EDE.0000000000000354

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  11 in total

1.  Effect heterogeneity and variable selection for standardizing causal effects to a target population.

Authors:  Anders Huitfeldt; Sonja A Swanson; Mats J Stensrud; Etsuji Suzuki
Journal:  Eur J Epidemiol       Date:  2019-10-26       Impact factor: 8.082

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

Authors:  Donna Spiegelman; Tyler J VanderWeele
Journal:  Am J Public Health       Date:  2017-07       Impact factor: 9.308

3.  Commentary: On Effect Measures, Heterogeneity, and the Laws of Nature.

Authors:  Orestis A Panagiotou; Thomas A Trikalinos
Journal:  Epidemiology       Date:  2015-09       Impact factor: 4.822

4.  Controversy and Debate : Questionable utility of the relative risk in clinical research: Paper 4 :Odds Ratios are far from "portable" - A call to use realistic models for effect variation in meta-analysis.

Authors:  Mengli Xiao; Haitao Chu; Stephen R Cole; Yong Chen; Richard F MacLehose; David B Richardson; Sander Greenland
Journal:  J Clin Epidemiol       Date:  2021-08-11       Impact factor: 6.437

5.  The Authors Respond.

Authors:  Charles Poole; Ian Shrier; Peng Ding; Tyler VanderWeele
Journal:  Epidemiology       Date:  2016-05       Impact factor: 4.822

6.  Recent trends in life expectancy for people with type 1 diabetes in Sweden.

Authors:  Dennis Petrie; Tom W C Lung; Aidin Rawshani; Andrew J Palmer; Ann-Marie Svensson; Björn Eliasson; Philip Clarke
Journal:  Diabetologia       Date:  2016-04-05       Impact factor: 10.122

Review 7.  Considerations when assessing heterogeneity of treatment effect in patient-centered outcomes research.

Authors:  Catherine R Lesko; Nicholas C Henderson; Ravi Varadhan
Journal:  J Clin Epidemiol       Date:  2018-04-11       Impact factor: 6.437

8.  Methodological assessment of systematic reviews and meta-analyses on COVID-19: A meta-epidemiological study.

Authors:  Kristine J Rosenberger; Chang Xu; Lifeng Lin
Journal:  J Eval Clin Pract       Date:  2021-05-05       Impact factor: 2.336

9.  The Interaction Continuum.

Authors:  Tyler J VanderWeele
Journal:  Epidemiology       Date:  2019-09       Impact factor: 4.822

Review 10.  Understanding the Assumptions Underlying Instrumental Variable Analyses: a Brief Review of Falsification Strategies and Related Tools.

Authors:  Jeremy Labrecque; Sonja A Swanson
Journal:  Curr Epidemiol Rep       Date:  2018-06-22
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