IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v56y2012i12p4180-4189.html
   My bibliography  Save this article

Model-based estimation of the attributable risk: A loglinear approach

Author

Listed:
  • Cox, Christopher
  • Li, Xiuhong

Abstract

This paper considers model-based methods for estimation of the adjusted attributable risk (AR) in both case-control and cohort studies. An earlier review discussed approaches for both types of studies, using the standard logistic regression model for case-control studies, and for cohort studies proposing the equivalent Poisson model in order to account for the additional variability in estimating the distribution of exposures and covariates from the data. In this paper, we revisit case-control studies, arguing for the equivalent Poisson model in this case as well. Using the delta method with the Poisson model, we provide general expressions for the asymptotic variance of the AR for both types of studies. This includes the generalized AR, which extends the original idea of attributable risk to the case where the exposure is not completely eliminated. These variance expressions can be easily programmed in any statistical package that includes Poisson regression and has capabilities for simple matrix algebra. In addition, we discuss computation of standard errors and confidence limits using bootstrap resampling. For cohort studies, use of the bootstrap allows binary regression models with link functions other than the logit.

Suggested Citation

  • Cox, Christopher & Li, Xiuhong, 2012. "Model-based estimation of the attributable risk: A loglinear approach," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4180-4189.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:12:p:4180-4189
    DOI: 10.1016/j.csda.2012.04.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947312001818
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2012.04.017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Barry I. Graubard & Thomas R. Fears, 2005. "Standard Errors for Attributable Risk for Simple and Complex Sample Designs," Biometrics, The International Biometric Society, vol. 61(3), pages 847-855, September.
    2. Anthony R. Brady, 1998. "Adjusted population attributable fractions from logistic regression," Stata Technical Bulletin, StataCorp LP, vol. 7(42).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mark S. Pearce & Heather O. Dickinson & Murray Aitkin & Louise Parker, 2002. "Still‐births among the offspring of male radiation workers at the Sellafield nuclear reprocessing plant: detailed results and statistical aspects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(3), pages 523-548, October.
    2. Yixin Wang & Ying Qing Chen, 2019. "Estimating Attributable Life Expectancy Under the Proportional Mean Residual Life Model," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 659-676, December.
    3. Pfeiffer, R.M. & Petracci, E., 2011. "Variance computations for functionals of absolute risk estimates," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 807-812, July.
    4. Lola Etievant & Mitchell H. Gail, 2024. "Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(3), pages 572-599, July.
    5. Anna V Wilkinson & Michael D Swartz & Xiaoying Yu & Margaret R Spitz & Sanjay Shete, 2013. "Cigarette Experimentation and the Population Attributable Fraction for Associated Genetic and Non-Genetic Risk Factors," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-6, January.
    6. Yang Liu & Brenda O. Hoppe & Matteo Convertino, 2018. "Threshold Evaluation of Emergency Risk Communication for Health Risks Related to Hazardous Ambient Temperature," Risk Analysis, John Wiley & Sons, vol. 38(10), pages 2208-2221, October.
    7. Kovalchik, Stephanie & Varadhan, Ravi, 2013. "Fitting Additive Binomial Regression Models with the R Package blm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i01).
    8. Roger Newson, 2012. "Scenario comparisons: How much good can we do?," United Kingdom Stata Users' Group Meetings 2012 01, Stata Users Group.
    9. Jason Brinkley & Anastasios Tsiatis & Kevin J. Anstrom, 2010. "A Generalized Estimator of the Attributable Benefit of an Optimal Treatment Regime," Biometrics, The International Biometric Society, vol. 66(2), pages 512-522, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:56:y:2012:i:12:p:4180-4189. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.