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Optimal allocation in stratified cluster-based outcome-dependent sampling designs.

Sara Sauer1, Bethany Hedt-Gauthier1,2, Sebastien Haneuse1

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Statistics in Medicine
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

Finite resources in public health research necessitate strategic sampling. This study introduces optimal allocation for clustered sampling designs, balancing efficiency gains against potential parameter trade-offs.

Keywords:
Health Management Information Systemscluster-based samplinggeneralized estimating equationsoptimal allocationoutcome-dependent sampling

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Area of Science:

  • Public Health Research
  • Biostatistics
  • Epidemiology

Background:

  • Limited resources in public health research require careful study design decisions for sampling.
  • Clustered study units (e.g., patients in clinics) may benefit from cluster sampling, especially with high travel costs.
  • Routinely available aggregated data can inform cluster sampling strategies for improved efficiency.

Purpose of the Study:

  • To derive formulas for optimal resource allocation in stratified cluster-based outcome-dependent sampling.
  • To address scenarios with a primary parameter of interest and scenarios with multiple parameters of interest.
  • To evaluate the finite population performance of the proposed optimal allocation framework.

Main Methods:

  • Derivation of formulas for optimal allocation in single-stage stratified cluster-based outcome-dependent sampling.
  • Specification of a marginal mean model to guide sampling decisions.
  • Comprehensive simulation study to assess finite population performance.

Main Results:

  • Optimizing for a single parameter offers efficiency gains over balanced and simple random sampling but may lead to losses for other parameters.
  • Simultaneous optimization for multiple parameters results in smaller efficiency gains but mitigates losses across parameters.
  • Design stage trade-offs are crucial for balancing efficiency and parameter estimation.

Conclusions:

  • The proposed optimal allocation framework provides a method for efficient cluster sampling in resource-limited public health research.
  • Strategic design choices are necessary to balance the estimation of single versus multiple model parameters.
  • Utilizing routinely available data can enhance the efficiency of cluster-based sampling designs.