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Related Experiment Video

Updated: Sep 5, 2025

Human Blastocyst Biopsy and Vitrification

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Published on: July 26, 2019

Viewpoint: Do not resurrect the two-stage procedure.

¹School of Health and Related Research, Medical Statistics Group, University of Sheffield, Edinburgh, UK.

Pharmaceutical Statistics

|July 12, 2022

Summary

This summary is machine-generated.

The two-stage procedure for analyzing cross-over trials is unacceptable, as demonstrated by Peter Freeman in 1989. This note uses simulations to explain why this flawed method should be avoided in statistical analysis.

Keywords:

cross-over trial error inflation inference pretesting

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Area of Science:

Biostatistics
Clinical Trial Design

Background:

The two-stage procedure for analyzing cross-over trials, widely accepted previously, was challenged by Peter Freeman in 1989.
Freeman highlighted serious flaws, leading to its rejection by many statisticians.
Despite its known issues, the two-stage procedure is still encountered in practice.

Purpose of the Study:

To explain the statistical flaws inherent in the two-stage procedure for cross-over trial analysis.
To reinforce why this method is unacceptable and should be discontinued.
To advocate for the use of alternative, statistically sound methods like the Bayesian approach recommended by Grieve.

Main Methods:

A simple simulation was employed to illustrate the deficiencies of the two-stage procedure.
The simulation aimed to provide a clear, understandable demonstration of the flaws identified by Freeman.
Comparison with recommended Bayesian methods was implicitly considered.

Main Results:

The simulation confirmed the unacceptability of the two-stage procedure.
The results visually and statistically demonstrate the critical errors associated with its application.
The findings underscore the validity of Freeman's original critique.

Conclusions:

The two-stage procedure for cross-over trials is statistically unsound and should not be used.
Peter Freeman's 1989 findings remain relevant, highlighting persistent issues in statistical practice.
Researchers should adopt robust methods, such as Bayesian analysis, for cross-over trial evaluations.