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Sample size calculation for cluster randomized trials with zero-inflated count outcomes.

Zhengyang Zhou1, Dateng Li2, Song Zhang3

  • 1Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, Texas, USA.

Statistics in Medicine
|February 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new sample size method for cluster randomized trials (CRTs) with zero-inflated count outcomes. The method accurately calculates sample sizes, improving power analysis for studies with many zero values.

Keywords:
cluster randomized trialsgeneralized estimating equationmarginalized modelssample sizezero-inflated outcomes

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Public Health Research

Background:

  • Cluster randomized trials (CRTs) are common in medical and public health research.
  • Count outcomes in clinical settings often exhibit excessive zero values (zero-inflation).
  • Traditional sample size calculations for count data may be insufficient with zero-inflation.

Purpose of the Study:

  • To develop a novel sample size calculation method for CRTs with zero-inflated count outcomes.
  • To address the limitations of existing power analysis methods in the presence of excess zeros.
  • To provide a robust approach for planning CRTs with complex count data.

Main Methods:

  • Developed a sample size method based on Generalized Estimating Equations (GEE) regression.
  • Modeled the marginal mean of a zero-inflated Poisson outcome directly.
  • Derived a closed-form sample size formula accounting for zero-inflation, intra-cluster correlation (ICC), unbalanced randomization, and cluster size variability.
  • Incorporated robust methods like t-distribution approximation and Jackknife variance estimation for small sample sizes.

Main Results:

  • The proposed sample size method accurately accounts for zero-inflation and clustering effects.
  • The formula provides a reliable basis for sample size determination in relevant CRTs.
  • Simulations confirmed the method's performance and robustness, especially in small sample scenarios.
  • An application example demonstrated its utility in a real-world clinical trial.

Conclusions:

  • The presented sample size method offers a statistically sound approach for CRTs with zero-inflated count data.
  • This method enhances the accuracy of power calculations, crucial for efficient trial design.
  • Researchers can confidently apply this method to improve planning and resource allocation in relevant studies.