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Power and Sample Size for Longitudinal Models in R - The longpower Package and Shiny App.

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This summary is machine-generated.

Calculating sample sizes for longitudinal studies is crucial for reliable medical research. This study provides formulas and a user-friendly app to aid researchers in determining appropriate sample sizes for continuous longitudinal data, including Alzheimer's clinical trials.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Medical Research Methodology

Background:

  • Longitudinal studies are essential in medical research for tracking changes over time.
  • Accurate sample size calculation is vital for ensuring sufficient statistical power and valid inferences in these studies.
  • Existing methodologies for sample size determination in longitudinal studies are complex and depend on various factors.

Purpose of the Study:

  • To review and present sample size formulas for continuous longitudinal data.
  • To introduce a practical tool (Shiny app) for researchers to calculate sample sizes.
  • To facilitate sample size estimation for Alzheimer's disease clinical trials using ADNI data.

Main Methods:

  • Literature review of sample size calculation methodologies for longitudinal studies.
  • Development of sample size formulas for continuous longitudinal data.
  • Creation of a Shiny application for interactive sample size estimation.
  • Utilizing Alzheimer's Disease Neuroimaging Initiative (ADNI) data for specific parameter estimation.

Main Results:

  • Formulas and illustrative examples for calculating sample sizes for continuous longitudinal data are provided.
  • A Shiny app is introduced, enabling researchers to input pilot estimates for sample size calculations.
  • The app can estimate pilot parameters for Alzheimer's studies using ADNI data.
  • Sample size and power curves can be generated to assess study design operating characteristics.

Conclusions:

  • The developed package and Shiny app simplify sample size determination for longitudinal studies.
  • These tools enhance the ability of researchers to design robust clinical trials, particularly for Alzheimer's disease.
  • The resources aid in assessing study design efficiency for longitudinal continuous outcomes.