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Related Experiment Videos

A SAS macro for sample size adjustment and randomization test for internal pilot study.

Suzhen Wang1, Jielai Xia, Lili Yu

  • 1Department of Health Statistics, Faculty of Preventative Medicine, Fourth Military Medical University, Xi'an, Shaanxi 710032, China.

Computer Methods and Programs in Biomedicine
|January 15, 2008
PubMed
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Internal pilot studies help adjust clinical trial sample sizes by re-estimating variance. Using blind variance estimators and randomization tests preserves blinding and controls Type I error rates effectively.

Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Statistical Inference

Background:

  • Clinical trial sample size is crucial for statistical power.
  • Over/underestimation of planned variance leads to inadequate sample sizes.
  • Internal pilot studies offer a solution by allowing interim sample size adjustments.

Purpose of the Study:

  • To address Type I error inflation in internal pilot studies.
  • To propose a method preserving blinding while controlling Type I error.
  • To introduce a SAS macro for simulating sample size adjustment and hypothesis testing.

Main Methods:

  • Utilizing information from patients recruited up to an interim stage.
  • Re-estimating variance and recalculating sample size based on interim data.

Related Experiment Videos

  • Employing blind variance estimators for sample size adjustment.
  • Implementing a randomization test for final hypothesis testing.

    • Main Results:

    • Standard t-tests can inflate Type I error rates with adjusted sample sizes.
    • Blind variance estimators combined with randomization tests maintain Type I error control.
    • The proposed method preserves the integrity of the internal pilot study's blinding.

    Conclusions:

    • Internal pilot studies are valuable for optimizing clinical trial sample sizes.
    • Blind variance estimation and randomization tests are essential for accurate hypothesis testing.
    • A SAS macro facilitates the simulation and implementation of this robust methodology.