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Model-based estimation of the attributable fraction for cross-sectional, case–control and cohort studies using the R package AF

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Abstract

The attributable fraction (or attributable risk) is a widely used measure that quantifies the public health impact of an exposure on an outcome. Even though the theory for AF estimation is well developed, there has been a lack of up-to-date software implementations. The aim of this article is to present a new R package for AF estimation with binary exposures. The package AF allows for confounder-adjusted estimation of the AF for the three major study designs: cross-sectional, (possibly matched) case–control and cohort. The article is divided into theoretical sections and applied sections. In the theoretical sections we describe how the confounder-adjusted AF is estimated for each specific study design. These sections serve as a brief but self-consistent tutorial in AF estimation. In the applied sections we use real data examples to illustrate how the AF package is used. All datasets in these examples are publicly available and included in the AF package, so readers can easily replicate all analyses.

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References

  1. Poole C. A history of the population attributable fraction and related measures. Ann Epidemiol. 2015;25(3):147–54.

    Article  PubMed  Google Scholar 

  2. Levin ML. The occurrence of lung cancer in man. Acta Union Int Contr. 1953;9(3):531–41.

    CAS  Google Scholar 

  3. Chen YQ, Hu C, Wang Y. Attributable risk function in the proportional hazards model for censored time-to-event. Biostatistics. 2006;7(4):515–29.

    Article  PubMed  Google Scholar 

  4. Chen L, Lin DY, Zeng D. Attributable fraction functions for censored event times. Biometrika. 2010;97(3):713–26.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Sjölander A, Vansteelandt S. Doubly robust estimation of attributable fractions in survival analysis. Stat Methods Med Res. 2014. doi:10.1177/0962280214564003.

  6. Sturmans F, Mulder PG, Valkenburg HA. Estimation of the possible effect of interventive measures in the area of ischemic heart diseases by the attributable risk percentage. Am J Epidemiol. 1977;105(3):281–9.

    CAS  PubMed  Google Scholar 

  7. Deubner DC, Wilkinson WE, Helms MJ, Tyroler HA, Hames CG. Logistic model estimation of death attributable to risk factors for cardiovascular disease in Evans County. Ga Am J Epidemiol. 1980;112(1):135–43.

    CAS  Google Scholar 

  8. Greenland S, Drescher K. Maximum likelihood estimation of the attributable fraction from logistic models. Biometrics. 1993;49(3):865–72.

    Article  CAS  PubMed  Google Scholar 

  9. Sjölander A, Vansteelandt S. Doubly robust estimation of attributable fractions. Biostatistics. 2011;12(1):112–21.

    Article  PubMed  Google Scholar 

  10. Miettinen OS. Proportion of disease caused or prevented by a given exposure, trait or intervention. Am J Epidemiol. 1974;99(5):325–32.

    CAS  PubMed  Google Scholar 

  11. Bruzzi P, Green SB, Byar DP, Brinton LA, Schairer C. Estimating the population attributable risk for multiple risk factors using case–control data. Am J Epidemiol. 1985;122(5):904–14.

    CAS  PubMed  Google Scholar 

  12. Core Team R. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2015. https://www.r-project.org/. Accessed 25 Nov 2015.

  13. Nunes T, Heuer C, Marshall J, Sanchez J, Thornton R, Reiczigel J, Robison-Cox J, Sebastiani P, Solymos P, Yoshida K, Jones G, Firestone SPaS. epiR: Tools for the analysis of epidemiological data. CRAN. 2015. https://cran.r-project.org/web/packages/epiR/index.html. Accessed 25 Nov 2015.

  14. Schenck L, Atkinson E, Crowson C, Therneau T. attribrisk: population attributable risk. CRAN. 2014. https://cran.r-project.org/web/packages/attribrisk/index.html. Accessed 25 Nov 2015.

  15. Chen L. paf: attributable fraction function for censored survival data. CRAN. 2014. https://cran.r-project.org/web/packages/paf/index.html. Accessed 25 Nov 2015.

  16. Juul S, Frydenberg M. An introduction to Stata for health researchers. 3rd ed. College Station, Texas: Stata Press; 2010.

    Google Scholar 

  17. Lehnert-Batar A, Pfahlberg A, Gefeller O. Comparison of confidence intervals for adjusted attributable risk estimates under multinomial sampling. Biom J. 2006;48(5):805–19.

    Article  PubMed  Google Scholar 

  18. De Jong UW, Breslow N, Hong JG, Sridharan M, Shanmugaratnam K. Aetiological factors in oesophageal cancer in Singapore Chinese. Int J Cancer. 1974;13(3):291–303.

    Article  PubMed  Google Scholar 

  19. Cox DR. Regression models and life-tables. J R Stat Soc Ser B Stat Methodol. 1972;34(2):187–220.

    Google Scholar 

  20. Breslow N. Discussion of the paper by D. R. Cox. J R Stat Soc B. 1972;34:216–7.

    Google Scholar 

  21. Sauerbrei W, Royston P, Look M. A new proposal for multivariable modelling of time-varying effects in survival data based on fractional polynomial time-transformation. Biom J. 2007;49(3):453–73.

    Article  PubMed  Google Scholar 

  22. Royston P, Lambert PC. Flexible Parametric survival analysis using Stata: beyond the Cox model. 1st ed. College Station, Texas: Stata Press; 2011.

    Google Scholar 

  23. Eide GE, Gefeller O. Sequential and average attributable fractions as aids in the selection of preventive strategies. J Clin Epidemiol. 1995;48(5):645–55.

    Article  CAS  PubMed  Google Scholar 

  24. Rämsch C, Pfahlberg AB, Gefeller O. Point and interval estimation of partial attributable risks from case–control data using the R-package ’pARccs’. Comput Meth Prog Biol. 2009;94(1):88–95.

    Article  Google Scholar 

  25. Morgenstern H, Bursic ES. A method for using epidemiologic data to estimate the potential impact of an intervention on the health status of a target population. J Commun Health. 1982;7(4):292–309.

    Article  CAS  Google Scholar 

  26. Drescher K, Becher H. Estimating the generalized impact fraction from case–control data. Biometrics. 1997;53(3):1170–6.

    Article  CAS  PubMed  Google Scholar 

  27. Taguri M, Matsuyama Y, Ohashi Y, Harada A, Ueshima H. Doubly robust estimation of the generalized impact fraction. Biostatistics. 2012;13(3):455–67.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Karolinska Institute, Nobels väg 12A, 171 77, Stockholm, Sweden

    Elisabeth Dahlqwist, Johan Zetterqvist, Yudi Pawitan & Arvid Sjölander

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  1. Elisabeth Dahlqwist
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  2. Johan Zetterqvist
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  3. Yudi Pawitan
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  4. Arvid Sjölander
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Correspondence to Elisabeth Dahlqwist.

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Dahlqwist, E., Zetterqvist, J., Pawitan, Y. et al. Model-based estimation of the attributable fraction for cross-sectional, case–control and cohort studies using the R package AF . Eur J Epidemiol 31, 575–582 (2016). https://doi.org/10.1007/s10654-016-0137-7

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  • DOI: https://doi.org/10.1007/s10654-016-0137-7

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