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A Comparative Study of Parametric and Nonparametric Estimates of the Attributable Fraction for a Semi-continuous Exposure

Author

Listed:
  • Wang Wei

    (Center for Outcomes Research, Children's Hospital of Philadelphia)

  • Small Dylan

    (Department of Statistics, the Wharton School, University of Pennsylvania)

Abstract

The attributable fraction of a disease due to an exposure is the fraction of disease cases in a population that can be attributed to that exposure. We consider the attributable fraction for a semi-continuous exposure, that is an exposure for which a clump of people have zero exposure and the rest of the people have a continuously distributed positive exposure. Estimation of the attributable fraction involves estimating the conditional probability of having the disease given the exposure. Three main approaches to estimating the probability function are (1) a classical method based on sample averages; (2) parametric regression methods such as logistic regression models and power models; and (3) nonparametric regression methods including local linear smoothing and isotonic regression. We compare performance of these methods in estimating the attributable fraction for a semi-continuous exposure in a simulation study and in an example.

Suggested Citation

  • Wang Wei & Small Dylan, 2012. "A Comparative Study of Parametric and Nonparametric Estimates of the Attributable Fraction for a Semi-continuous Exposure," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-22, November.
    • Handle: RePEc:bpj:ijbist:v:8:y:2012:i:1:n:32
    DOI: 10.1515/1557-4679.1389
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    References listed on IDEAS

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    1. Dette, Holger & Neumeyer, Natalie & Pilz, Kay F., 2005. "A Note on Nonparametric Estimation of the Effective Dose in Quantal Bioassay," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 503-510, June.
    2. Signorini, D.F. & Jones, M.C., 2004. "Kernel Estimators for Univariate Binary Regression," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 119-126, January.
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