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Maximin optimal designs for cluster randomized trials.

Sheng Wu1, Weng Kee Wong1, Catherine M Crespi1

  • 1Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, California 90095-1772, U.S.A.

Biometrics
|February 10, 2017
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Summary
This summary is machine-generated.

This study introduces a robust design for cluster randomized trials (CRTs) that optimizes cost efficiency. The maximin optimal design is more reliable than standard approaches when dealing with unknown costs and intraclass correlation coefficients (ICCs).

Keywords:
Balanced designBinary outcomeIntraclass correlation coefficientRelative cost efficiencyRobust designSampling ratio

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Public Health Research

Background:

  • Cluster randomized trials (CRTs) are essential for evaluating interventions in group-level settings.
  • Standard CRT designs may face challenges with unequal unit costs and intraclass correlation coefficients (ICCs) between study arms.
  • Assessing intervention efficacy requires precise estimation, often complicated by variability in trial parameters.

Purpose of the Study:

  • To develop and evaluate a cost-efficient design for CRTs with binary outcomes and potentially unequal unit costs and ICCs.
  • To propose a maximin optimal design that is robust to uncertainty in anticipated success rates and ICCs.
  • To compare the efficiency and robustness of the proposed design against traditional balanced designs using a real-world example.

Main Methods:

  • Proposed a cost efficiency (CE) maximizing design, defined as the ratio of efficacy measure precision to study cost.
  • Developed a maximin optimal design to mitigate sensitivity to unknown ICCs and success rates by allowing ranges of values.
  • Derived and analyzed maximin optimal designs for risk difference, relative risk, and odds ratio efficacy measures.

Main Results:

  • The proposed maximin optimal design demonstrates superior robustness and efficiency compared to standard balanced designs.
  • Balanced designs can be inefficient and highly sensitive to mis-specifications of ICCs and success rates.
  • A cancer control and prevention trial example highlighted the practical advantages of the maximin optimal approach.

Conclusions:

  • The maximin optimal design offers a more reliable and cost-efficient strategy for CRTs with binary outcomes, especially when parameters are uncertain.
  • Standard balanced designs may lead to suboptimal resource allocation and biased results due to their sensitivity to parameter variations.
  • The findings advocate for the adoption of maximin optimal designs in CRTs to enhance statistical power and economic viability.