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Related Experiment Videos

Two-stage sampling for etiologic studies. Sample size and power

D Schaubel1, J Hanley, J P Collet

  • 1Department of Epidemiology and Biostatistics, Faculty of Medicine, McGill University, Montréal, Québec, Canada.

American Journal of Epidemiology
|September 18, 1997
PubMed
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Two-stage sampling efficiently collects confounder data from large existing databases for epidemiologic research. This method helps determine the necessary sample size for accurate etiologic studies when complete data is unavailable.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Informatics

Background:

  • Preexisting computerized databases offer valuable epidemiologic data but often lack comprehensive confounder information.
  • Collecting confounder data for entire large databases is often infeasible for etiologic research.
  • Two-stage sampling presents an efficient strategy for acquiring necessary confounder data.

Purpose of the Study:

  • To present methods for determining the required stage 2 sample size in two-stage sampling.
  • To provide tools for estimating sample size for confounder data collection in large databases.
  • To facilitate the use of preexisting databases in etiologic research.

Main Methods:

  • Two-stage sampling involves collecting exposure and disease data at stage 1 for the entire population.

Related Experiment Videos

  • Confounder data is collected at stage 2 from probability-selected subsamples.
  • Logistic regression models are applied with corrected parameter estimates and variances.

    • Main Results:

    • Methods for determining stage 2 sample size are presented for categorical exposure and confounding variables.
    • Sample size tables, power curves, and a computer program are developed for binary exposure and confounder scenarios.
    • The study provides a basis for estimating the number of participants needed for second-stage confounder data collection.

    Conclusions:

    • Two-stage sampling is an efficient method for utilizing large, incomplete preexisting databases in epidemiologic studies.
    • The developed methods and tools support accurate sample size estimation for confounder data collection.
    • This approach enhances the potential of existing databases for etiologic research, especially with increasing data availability.