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Survey design and analysis considerations when utilizing misclassified sampling strata.

Aya A Mitani1, Nathaniel D Mercaldo2, Sebastien Haneuse3

  • 1Division of Biostatistics, University of Toronto Dalla Lana School of Public Health, Toronto, Canada. aya.mitani@utoronto.ca.

BMC Medical Research Methodology
|July 12, 2021
PubMed
Summary
This summary is machine-generated.

Disproportionate stratified sampling improves estimates for minority groups in biobank studies. Choose design-agnostic analysis for non-differential misclassification and design-based analysis for differential misclassification in complex survey designs.

Keywords:
Complex surveyDesign-based analysisDisproportionate stratified samplingModel-based analysisStratum misclassification

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Area of Science:

  • Biostatistics
  • Survey Methodology
  • Genomics and Health

Background:

  • A multi-center survey explored patient views on biobank participation, focusing on racial and ethnic minorities.
  • Disproportionate stratified sampling was used to increase minority representation, despite potential inaccuracies in electronic health records (EHR) used for strata definition.
  • The study investigates the impact of sampling strata misclassification within complex survey designs.

Purpose of the Study:

  • To evaluate the validity and precision of different analysis approaches under non-differential and differential strata misclassification.
  • To compare the efficiency of disproportionate stratified sampling against simple random sampling with varying degrees of strata misclassification.

Main Methods:

  • Compared three common analysis approaches (design-agnostic, model-based, design-based) for strata defined by primary exposure.
  • Assessed parameter estimates and standard errors under non-differential and differential misclassification scenarios.
  • Evaluated precision gains/losses of disproportionate stratified sampling versus simple random sampling.

Main Results:

  • Disproportionate stratified sampling yielded more efficient estimates for rare subgroups compared to simple random sampling.
  • For non-differential misclassification, design-agnostic analysis showed the lowest standard errors.
  • Differential misclassification necessitated design-based methods for valid parameter estimates within sampling strata.

Conclusions:

  • Recommends disproportionate stratified sampling over simple random sampling for inferences on rare subgroups in complex surveys, even with misclassified strata.
  • Advocates for design-agnostic analysis when strata misclassification is non-differential.
  • Recommends design-based analyses when strata misclassification is differential.