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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Practical strategies for operationalizing optimal allocation in stratified cluster-based outcome-dependent sampling

Sara Sauer1,2, Bethany Hedt-Gauthier1,2, Sebastien Haneuse2

  • 1Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA.

Statistics in Medicine
|January 17, 2023
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Summary

This study introduces an adaptive sampling method for rare outcomes, improving efficiency in cluster-based studies. The multiple imputation approach demonstrated near-optimal sample size allocation, enhancing statistical power.

Keywords:
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Area of Science:

  • Biostatistics
  • Epidemiology
  • Public Health Research

Background:

  • Cluster-based outcome-dependent sampling (ODS) offers efficiency gains for rare outcomes under resource constraints.
  • Optimal sample size allocation across strata can enhance estimation efficiency for generalized estimating equations.
  • Current optimal allocation formulas are difficult to implement due to unknown practical parameters.

Purpose of the Study:

  • To develop and evaluate a two-wave adaptive sampling approach for cluster-based ODS.
  • To compare inverse-probability weighting (IPW) and multiple imputation (MI) for estimating optimal second-wave sample sizes.
  • To assess the performance of adaptive sampling in improving efficiency for rare outcomes.

Main Methods:

  • A two-wave adaptive sampling design was proposed.
  • First-wave data was used to estimate stratum-specific sample sizes for the second wave.
  • Two estimation strategies were considered: inverse-probability weighting (IPW) and multiple imputation (MI).

Main Results:

  • The adaptive sampling approach demonstrated good performance in simulations.
  • The multiple imputation (MI) strategy yielded near-optimal designs across various covariate types.
  • The inverse-probability weighting (IPW) strategy showed mixed performance.

Conclusions:

  • Adaptive sampling provides a practical solution for optimizing cluster-based ODS designs.
  • Multiple imputation is a robust method for estimating optimal sample sizes in adaptive cluster sampling.
  • The proposed methods were illustrated using data from Zanzibar, Tanzania.