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Regression‐based estimation of the false negative fraction when multiple negatives are unverified

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  • Chris J. Lloyd
  • Donald J. Frommer

Abstract

Summary. The paper describes a method of estimating the false negative fraction of a multiple‐screening test when individuals who test negatively on all K tests do not have their true disease status verified. The method proposed makes no explicit assumptions about the underlying heterogeneity of the population or about serial correlation of test results within an individual. Rather, it is based on estimating false negative fractions conditionally on observed diagnostic histories and extrapolating the observed patterns in these empirical frequencies by using logistic regression. The method is illustrated on, and motivated by, data on a multiple‐screening test for bowel cancer.

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  • Chris J. Lloyd & Donald J. Frommer, 2004. "Regression‐based estimation of the false negative fraction when multiple negatives are unverified," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(4), pages 619-631, November.
    • Handle: RePEc:bla:jorssc:v:53:y:2004:i:4:p:619-631
    DOI: 10.1111/j.1467-9876.2004.05303.x
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    Cited by:

    1. Orasa Anan & Dankmar Böhning & Antonello Maruotti, 2019. "On the Turing estimator in capture–recapture count data under the geometric distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(2), pages 149-172, March.
    2. Marco Alfò & Dankmar Böhning & Irene Rocchetti, 2021. "Upper bound estimators of the population size based on ordinal models for capture‐recapture experiments," Biometrics, The International Biometric Society, vol. 77(1), pages 237-248, March.

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