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Sample size/power calculation for stratified case-cohort design.

Wenrong Hu1, Jianwen Cai, Donglin Zeng

  • 1Applied Statistics, Department of Mathematical Sciences, The University of Memphis, Memphis, TN, U.S.A.; Department of Biostatistics, CSL Behring, King of Prussia, PA, U.S.A.

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Summary
This summary is machine-generated.

This study introduces sample size calculations for stratified case-cohort designs, crucial for epidemiological risk factor assessment. The new methods are validated through simulations for low disease prevalence studies.

Keywords:
case-cohort designpower calculationsample sizesampling techniquestratified case-cohort design

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health Research

Background:

  • The case-cohort (CC) study design is established for risk factor assessment in epidemiology and disease prevention trials, particularly for rare diseases.
  • Sample size and power calculations for the stratified case-cohort (SCC) design have not been previously established.
  • Existing CC designs require adaptation for stratified analyses to enhance statistical power and efficiency.

Purpose of the Study:

  • To derive sample size and power calculation methods for the stratified case-cohort (SCC) study design.
  • To evaluate the validity and efficiency of the proposed methods using simulation studies.
  • To optimize sampling strategies within the SCC design and compare them with existing techniques.

Main Methods:

  • Derivation of sample size and power calculations based on a novel stratified test statistic for SCC designs.
  • Conducting simulation studies to assess the performance of the proposed methods under various conditions, including low disease rates and small sub-cohort sampling fractions.
  • Comparing different sampling strategies (proportional, balanced, and optimized) within the SCC framework.

Main Results:

  • The proposed sample size and power calculation methods for SCC designs are shown to be valid and efficient, especially when disease rates are low.
  • Simulation studies confirm the utility of the SCC design with small sub-cohort sampling fractions.
  • Optimization of sampling techniques in SCC designs offers advantages over traditional proportional and balanced sampling.

Conclusions:

  • The developed methods provide essential tools for planning future epidemiological studies using the SCC design.
  • The SCC design, particularly with optimized sampling, offers a statistically sound and efficient approach for risk factor assessment in studies with rare diseases.
  • This work addresses a critical gap in the statistical methodology for case-cohort studies.