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Model-based screening by risk with application to Down's syndrome.

P Royston1, S G Thompson

  • 1Department of Medical Physics, Royal Postgraduate Medical School, London, U.K.

Statistics in Medicine
|January 30, 1992
PubMed
Summary
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This study introduces risk-based screening for disorders using likelihood ratios. It compares estimation methods and discusses how increased variable separation can paradoxically raise detection and false positive rates in screening.

Area of Science:

  • Medical screening
  • Biostatistics
  • Diagnostic testing

Background:

  • Screening identifies individuals at risk for disorders based on variable distributions.
  • Optimal screening uses a risk cut-off value to classify individuals.
  • Likelihood ratios are central to deriving risk estimates.

Purpose of the Study:

  • To summarize risk derivation from likelihood ratios for disorder screening.
  • To compare direct estimation, logistic regression, and distribution modeling for likelihood ratio estimation.
  • To explore the properties and application of risk-based screening, including antenatal screening for Down's syndrome.

Main Methods:

  • Derivation of risk from likelihood ratios.
  • Comparison of three likelihood ratio estimation methods: direct estimation, logistic regression, and distribution modeling.

Related Experiment Videos

  • Application of quadratic discrimination for multivariate normal distributions.
  • Estimation of detection and false positive rates using specified risk cut-offs and shrinkage.

    • Main Results:

    • Risk-based screening is equivalent to quadratic discrimination for multivariate normal data.
    • Increased separation between affected and unaffected groups can paradoxically increase both detection and false positive rates.
    • Model-based approaches are preferred, but require addressing outliers and non-normality.

    Conclusions:

    • Model-based risk assessment is the preferred method for disorder screening.
    • Understanding the counter-intuitive properties of risk-based screening is crucial for accurate interpretation.
    • Careful handling of data distributions and outliers is necessary for reliable risk estimation and screening policy development.