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Power and sample size calculation of two-sample projection-based testing for sparsely observed functional data.

Salil Koner1, Sheng Luo1

  • 1Department of Biostatistics and Bioinformatics, Duke University, North Carolina, USA.

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Summary
This summary is machine-generated.

This study introduces a power and sample size (PASS) toolkit for projection-based testing of functional data, enhancing clinical trial design. The method is robust to missing data, proving valuable for Parkinson's disease research.

Keywords:
Azilect trialHypothesis testingLongitudinal dataSparse Functional data

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Area of Science:

  • Statistics
  • Biostatistics
  • Functional Data Analysis

Background:

  • Projection-based testing effectively analyzes mean trajectory differences in functional data.
  • Existing methods may lack comprehensive power and sample size calculations for complex data structures.

Purpose of the Study:

  • To derive the theoretical power function for projection-based testing.
  • To introduce a power and sample size (PASS) calculation toolkit for this method.
  • To assess the method's robustness and practical utility in clinical trial design.

Main Methods:

  • Derivation of the theoretical power function for projection-based testing.
  • Development of a comprehensive PASS toolkit.
  • Numerical simulations to evaluate statistical power and robustness to missing data.
  • Application to randomized controlled trials in Parkinson's disease.

Main Results:

  • The derived power function and PASS toolkit accommodate diverse group differences and covariance structures.
  • The projection-based testing method demonstrates robustness, maintaining statistical power even with missing observations ('missing-immune').
  • Practical utility is confirmed through analyses of Parkinson's disease clinical trials.

Conclusions:

  • The developed PASS toolkit significantly enhances the usability of projection-based testing.
  • The method's robustness and missing-immune property make it suitable for clinical trial design.
  • The R package fPASS facilitates practical implementation in biological and clinical research.