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Related Experiment Videos

Data adaptive interim modification of sample sizes for candidate-gene association studies.

André Scherag1, Hans-Helge Müller, Astrid Dempfle

  • 1Institute of Medical Biometry and Epidemiology, Clinic of Child and Adolescent Psychiatry, University of Marburg, Germany.

Human Heredity
|November 14, 2003
PubMed
Summary

This study introduces an adaptive group sequential procedure for genetic association studies, allowing flexible sample size reassessment. This method enhances statistical power while controlling type I error rates, offering a promising alternative to fixed designs.

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Area of Science:

  • Biostatistics
  • Genetic Epidemiology
  • Statistical Genetics

Background:

  • Conventional Transmission/Disequilibrium tests in candidate-gene association studies necessitate pre-specifying sample size for adequate power.
  • Difficulty in estimating genetic effect size beforehand complicates sample size calculation for these studies.
  • The need for sample size reassessment during a study is recognized.

Purpose of the Study:

  • To propose and evaluate an adaptive group sequential procedure for genetic association studies.
  • To allow for early stopping or sample size recalculation based on interim data.
  • To maintain statistical power and control type I error rates.

Main Methods:

  • An adaptive group sequential procedure, building on Müller and Schäfer's work, is proposed.

Related Experiment Videos

  • The method allows early rejection of the null hypothesis (H0) or sample size adjustment.
  • Monte Carlo simulations compare the adaptive design with fixed and conventional group sequential designs.

    • Main Results:

    • The adaptive procedure offers flexibility, enabling design modifications.
    • It stabilizes power characteristics by utilizing collected data.
    • Overall type I error is effectively controlled.

    Conclusions:

    • The adaptive group sequential procedure is a flexible and advantageous alternative to traditional fixed sample designs.
    • It provides a promising approach for optimizing sample size and power in genetic association studies.
    • This method facilitates more efficient study designs by incorporating interim analyses.