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Generalized sample size determination formulas for experimental research with hierarchical data.

Satoshi Usami1

  • 1Department of Psychology, University of Southern California, Los Angeles, CA, USA, usami_s@p.u-tokyo.ac.jp.

Behavior Research Methods
|November 8, 2013
PubMed
Summary
This summary is machine-generated.

This study provides new formulas for calculating sample sizes in hierarchical data research, crucial for ensuring statistical power in multisite-randomized trials (MRTs) and cluster-randomized trials (CRTs). The derived formulas aid researchers in planning experiments with nested data structures.

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Area of Science:

  • Statistics
  • Experimental Design
  • Biostatistics

Background:

  • Hierarchical data structures are common in research, with lower units nested within higher units.
  • Accurate sample size estimation is critical for achieving sufficient statistical power and precision in experimental research.
  • Existing methods for sample size calculation in hierarchical data have limitations.

Purpose of the Study:

  • To derive closed-form formulas for sample size determination in experimental research with hierarchical data.
  • To extend previous work by addressing both multisite-randomized trials (MRTs) and cluster-randomized trials (CRTs).
  • To provide tools for researchers to ensure adequate statistical power and confidence interval precision.

Main Methods:

  • Derivation of closed-form sample size formulas.
  • Application to three-level data using a random-intercept model.
  • Consideration of both balanced and unbalanced designs.
  • Focus on multisite-randomized trials (MRTs) and cluster-randomized trials (CRTs).

Main Results:

  • Novel closed-form formulas for sample size calculation in hierarchical data.
  • Formulas account for statistical power and confidence interval width.
  • Identification of lower bounds for the number of units at the highest levels of the hierarchy.
  • Applicability to both multisite-randomized trials (MRTs) and cluster-randomized trials (CRTs).

Conclusions:

  • The derived formulas offer a robust method for sample size estimation in complex hierarchical experimental designs.
  • These tools are essential for researchers conducting multisite-randomized trials (MRTs) and cluster-randomized trials (CRTs).
  • The findings contribute to more rigorous and efficient experimental research planning with nested data.