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Cost-efficient case-control cluster sampling designs for population-based epidemiological studies.

Thomas Ly1, Myles Cockburn1, Bryan Langholz1

  • 1University of Southern California, Keck School of Medicine, Department of Preventive Medicine, 2001 North Soto Street, Los Angeles, CA 90032, USA.

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|November 5, 2018
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Summary
This summary is machine-generated.

New cluster-based sampling methods offer cost-efficient ways to select participants for population studies. These designs reduce interviewer travel costs, making studies in dispersed areas more feasible and economical.

Keywords:
Cluster samplingIndividually-matched case-control DesignSampling weightsSurvey samplingTwo-stage sampling

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Survey Methodology

Background:

  • Population-based case-control studies require cost-efficient sampling, especially in geographically dispersed populations.
  • High interviewer travel costs associated with simple random sampling (SRS) can be prohibitive.

Purpose of the Study:

  • To present cluster-based, individually matched case-control designs for cost-efficient sampling of additional controls.
  • To reduce interviewer travel costs in population-based studies.

Main Methods:

  • Utilized cluster sampling principles, matching one case to three controls.
  • Employed a two-stage sampling approach: primary sampling clusters selected based on case-control status, followed by subsampling controls within clusters.
  • Computer simulations were used to compare designs.

Main Results:

  • Cluster-based designs provide unbiased rate ratio estimation.
  • Statistical efficiencies were comparable to SRS 1:1 and moderately less than SRS 1:3.
  • Efficiencies remained comparable to SRS 1:1 even with high intracluster correlation for the exposure variable.
  • Cost-efficiency analysis showed cluster-based designs were more cost-efficient than SRS 1:3.

Conclusions:

  • Cluster-based individually matched case-control designs are a cost-efficient alternative to SRS for sampling controls in population studies.
  • These methods effectively reduce interviewer travel costs without significantly compromising statistical efficiency.