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Attributable and unattributable risks and fractions and other scenario comparisons

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  • Roger B. Newson

    (National Heart and Lung Institute, Imperial College London)

Abstract

Scenarios are alternative versions of the same dataset with the same variables but different observations or values. Applied scientists frequently want to predict how much good an intervention will do by comparing outcomes from the same model between different scenarios. Alternatively, they may want to compare outcomes between different models applied to the same scenario, for instance, when standardizing statistics from different subpopulations to a common gender and age distribution. Standard Stata tools for scenario means and comparisons are margins and pwcompare. A suite of packages is presented for estimating scenario means and comparisons by using margins, together with normalizing and variance-stabilizing transformations implemented by using nlcom. margprev estimates marginal prevalences; marglmean estimates marginal arithmetic means; regpar estimates the difference between two marginal prevalences (the population attributable risk); punaf estimates the ratio between two marginal arithmetic means (the population unattributable fraction); and punafcc estimates a marginal mean between-scenario risk or hazard ratio for case–control or survival data (also known as a population unattributable fraction). The population unattributable fraction and its confidence limits are subtracted from 1 to estimate the population attributable fraction. Formulas and examples are presented, including an example from the Global Allergy and Asthma European Network. Copyright 2013 by StataCorp LP.

Suggested Citation

  • Roger B. Newson, 2013. "Attributable and unattributable risks and fractions and other scenario comparisons," Stata Journal, StataCorp LP, vol. 13(4), pages 672-698, December.
    • Handle: RePEc:tsj:stataj:v:13:y:2013:i:4:p:672-698
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    2. Doidge, James C & Higgins, Daryl J & Delfabbro, Paul & Edwards, Ben & Vassallo, Suzanne & Toumbourou, John W & Segal, Leonie, 2017. "Economic predictors of child maltreatment in an Australian population-based birth cohort," Children and Youth Services Review, Elsevier, vol. 72(C), pages 14-25.
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    6. Corsi, Daniel J. & Mejía-Guevara, Iván & Subramanian, S.V., 2016. "Risk factors for chronic undernutrition among children in India: Estimating relative importance, population attributable risk and fractions," Social Science & Medicine, Elsevier, vol. 157(C), pages 165-185.
    7. Brännström, Lars & Vinnerljung, Bo & Hjern, Anders, 2015. "Risk factors for teenage childbirths among child welfare clients: Findings from Sweden," Children and Youth Services Review, Elsevier, vol. 53(C), pages 44-51.
    8. Phuong H Nguyen & Sunny S Kim & Tuan T Nguyen & Lan M Tran & Nemat Hajeebhoy & Edward A Frongillo & Marie T Ruel & Rahul Rawat & Purnima Menon, 2016. "Supply- and Demand-Side Factors Influencing Utilization of Infant and Young Child Feeding Counselling Services in Viet Nam," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-17, March.
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    10. Brännström, Lars & Karlsson, Henrik & Vinnerljung, Bo & Hjern, Anders, 2018. "Childhood risk factors for disability pension among adult former Swedish child welfare clients: Same or different as for majority population peers?," Children and Youth Services Review, Elsevier, vol. 84(C), pages 94-102.

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