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Related Experiment Videos

Statistical power and optimal design for multisite randomized trials.

S W Raudenbush1, X Liu

  • 1School of Education, University of Michigan, Ann Arbor 48109, USA. rauden@umich.edu

Psychological Methods
|August 11, 2000
PubMed
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This study introduces a hierarchical linear model for multisite trials to optimize sample size and site allocation. It helps researchers understand treatment effects and variances across different research sites.

Area of Science:

  • Mental health research
  • Clinical psychology
  • Biostatistics

Background:

  • Multisite trials are crucial in mental health research for assessing average treatment impact and variance across sites.
  • Key design decisions involve sample size per site and the total number of sites, impacting study power.
  • Site characteristics can moderate treatment efficacy, necessitating careful study design.

Purpose of the Study:

  • To propose a standardized hierarchical linear model for power analysis in multisite trials.
  • To provide guidelines for optimal resource allocation within and between sites.
  • To illustrate these concepts using newly developed software.

Main Methods:

  • A standardized hierarchical linear model is proposed for power calculations.

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  • Rules of thumb for small, medium, and large effect sizes and treatment-by-site variance are adapted from J. Cohen (1988).
  • Optimal allocation of resources is considered based on variance components and costs.

    • Main Results:

    • The proposed model facilitates the assessment of average treatment effects, variance across sites, and moderation by site characteristics.
    • Guidelines are provided for determining optimal sample sizes and number of sites for desired statistical power.
    • The approach is generalizable to quasiexperiments with similar structures.

    Conclusions:

    • The hierarchical linear model offers a robust framework for designing and analyzing multisite trials.
    • Optimal resource allocation strategies can enhance the efficiency and power of such studies.
    • The developed software aids researchers in applying these design principles effectively.