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Related Experiment Videos

Population prevalence estimates from complex samples.

L A Beckett1, P A Scherr, D A Evans

  • 1Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115.

Journal of Clinical Epidemiology
|April 1, 1992
PubMed
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See all related articles

Estimating disease prevalence in complex population samples is challenging. Logistic regression effectively smooths prevalences for accurate subgroup and overall estimates, especially for conditions like Alzheimer's disease.

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Epidemiologic studies often employ complex sampling designs for case ascertainment and demographic matching.
  • Standard statistical software typically assumes simple random sampling, hindering prevalence estimation in complex designs.
  • Estimating population or subgroup prevalence can be difficult with existing software for complex samples.

Purpose of the Study:

  • To evaluate methods for estimating overall and subgroup prevalence from complex sample data.
  • To compare crude prevalences, direct standardization, and logistic regression-based standardization.
  • To assess the utility of logistic regression for smoothing prevalences in complex survey data.

Main Methods:

  • Employed crude prevalence estimation.
  • Utilized direct standardization of prevalences.

Related Experiment Videos

  • Applied standardization through logistic regression to smooth sampling group prevalences.
  • Illustrated methods using a complex sample for Alzheimer's disease prevalence in an urban community.
  • Conducted a simulation study to evaluate performance under various models.

    • Main Results:

    • Logistic regression smoothing of prevalences before standardization proved effective for overall prevalence estimation.
    • The accuracy of logistic regression-based estimation is contingent on the careful assessment of model fit.
    • Demonstrated application in estimating Alzheimer's disease prevalence.

    Conclusions:

    • Logistic regression offers an effective approach to smooth prevalences in complex samples for accurate estimation.
    • Careful validation of the logistic model's fit is crucial for reliable prevalence estimates.
    • The methods are applicable to estimating disease prevalence, such as Alzheimer's, in complex populations.