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Variance computations for functionals of absolute risk estimates

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  • Pfeiffer, R.M.
  • Petracci, E.

Abstract

We present a simple influence function based approach for computing the variances of estimates of absolute risk and functions of absolute risk. We apply this approach to criteria that assess the impact of changes in the risk factor distribution on absolute risk for an individual and at the population level. As an illustration we use an absolute risk prediction model for breast cancer that includes modifiable risk factors in addition to standard breast cancer risk factors. Influence function based variance estimates for absolute risk and the criteria are compared to bootstrap variance estimates.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:stapro:v:81:y:2011:i:7:p:807-812
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    References listed on IDEAS

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    1. de Uña-Álvarez, Jacobo & Meira-Machado, Luis F., 2008. "A simple estimator of the bivariate distribution function for censored gap times," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2440-2445, October.
    2. Stute, W., 1993. "Consistent Estimation Under Random Censorship When Covariables Are Present," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 89-103, April.
    3. Ming Yuan, 2005. "Semiparametric censorship model with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(2), pages 489-514, December.
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