Evaluating Public Health Interventions

7. Let the Subject Matter Choose the Effect Measure: Ratio, Difference, or Something Else Entirely

Bibliographic Data

ID11029615
AuthorsDonna Spiegelman (0000-0003-4006-4650, Donna Spiegelman is with the departments of Epidemiology, Biostatistics, Nutrition, and Global Health, Harvard T. H. Chan School of Public Health, Boston, MA. Donna Spiegelman and Molin Wang are also with the Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston. Polyna Khudyakov is with the Department of Epidemiology, Harvard T. H. Chan School of Public Health. Molin Wang is with the departments of Epidemiology, Biostatistics, and Medicine, Harvard T. H. Chan School of Public...), Polyna Khudyakov (Donna Spiegelman is with the departments of Epidemiology, Biostatistics, Nutrition, and Global Health, Harvard T. H. Chan School of Public Health, Boston, MA. Donna Spiegelman and Molin Wang are also with the Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston. Polyna Khudyakov is with the Department of Epidemiology, Harvard T. H. Chan School of Public Health. Molin Wang is with the departments of Epidemiology, Biostatistics, and Medicine, Harvard T. H. Chan School of Public...), Molin Wang (0000-0003-1951-8961, Donna Spiegelman is with the departments of Epidemiology, Biostatistics, Nutrition, and Global Health, Harvard T. H. Chan School of Public Health, Boston, MA. Donna Spiegelman and Molin Wang are also with the Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston. Polyna Khudyakov is with the Department of Epidemiology, Harvard T. H. Chan School of Public Health. Molin Wang is with the departments of Epidemiology, Biostatistics, and Medicine, Harvard T. H. Chan School of Public...), Tyler J Vanderweele (0000-0002-6112-0239, Donna Spiegelman is with the departments of Epidemiology, Biostatistics, Nutrition, and Global Health, Harvard T. H. Chan School of Public Health, Boston, MA. Donna Spiegelman and Molin Wang are also with the Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston. Polyna Khudyakov is with the Department of Epidemiology, Harvard T. H. Chan School of Public Health. Molin Wang is with the departments of Epidemiology, Biostatistics, and Medicine, Harvard T. H. Chan School of Public...)
Year2018
Volume108
Issue1
Pages73-76
Publication date2018-01-01
Open AccessNo
TypeARTICLE
VenueAmerican Journal of Public Health (JOURNAL)
Journal identifiersISSN: 0090-0036 • E-ISSN: 1541-0048
PublisherAmerican Public Health Association (PUBLISHER • US)
DOI10.2105/ajph.2017.304105
PMID29161073
OpenAlexW2768868661
LanguageEN
Citations received4
References cited17

We define measures of effect used in public health evaluations, which include the risk difference and the risk ratio, the population-attributable risk, years of life lost or gained, disability-adjusted life years, quality-adjusted life years, and the incremental cost-effectiveness ratio. Except for the risk ratio, all of these are absolute effect measures. For constructing externally generalizable absolute measures of effect when there is superior fit of the multiplicative model, we suggest using the multiplicative model to estimate relative risks, which will often be obtained in simple linear form with no interactions, and then converting these to the desired absolute measure. The externally generalizable absolute measure of effect can be obtained by suitably standardizing to the risk factor distribution of the population to which the results are to be generalized. External generalizability will often be compromised when absolute measures are computed from study populations with risk factor distributions different from those of the population to whom the results are to be generalized, even when these risk factors are not confounders of the intervention effect

Absolute risk reduction · Actuarial science · Confidence interval · Confounding · Econometrics · Economics · Environmental health · Generalizability theory · Multiplicative function · Population · Relative risk · Risk factor · Statistics · Advanced Causal Inference Techniques · Demography · Health Systems, Economic Evaluations, Quality of Life · Mathematics · Medicine · Statistical Methods and Bayesian Inference · Gerontology

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