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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Sample sizes for trials involving multiple correlated must-win comparisons.

Steven A Julious¹, Nikki E McIntyre

¹University of Sheffield-Medical Statistics Group, Health Services Research, ScHARR University of Sheffield, Regent Court, 30 Regent Steet, Sheffield, Yorkshire S14DA, United Kingdom. s.a.julious@sheffield.ac.uk

Pharmaceutical Statistics

|March 3, 2012

Summary

This summary is machine-generated.

This study introduces methods for calculating sample sizes in clinical trials with multiple correlated comparisons. It addresses Type II error inflation, ensuring adequate trial power when all comparisons must be significant.

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Area of Science:

Biostatistics
Clinical Trial Design

Background:

Controlling Type I error in multiple comparisons is standard in clinical trials.
Multiple comparisons can impact Type II error, reducing trial power if unaddressed.

Purpose of the Study:

To describe sample size calculations for clinical trials with multiple correlated comparisons.
To address the impact of multiplicity on Type II error in specific trial designs.

Main Methods:

Developed sample size calculation methods accounting for Type II error multiplicity.
Utilized bivariate Normal distribution for two comparisons.
Employed inflation factors for general cases (two or more comparisons).

Main Results:

Accounting for Type II error multiplicity is crucial when all comparisons must be significant.
Methods were derived for bivariate Normal distributions and generalized using inflation factors.
Sample size increases are modest when Type II error multiplicity is considered.

Conclusions:

The proposed methods provide a practical approach to sample size determination in complex clinical trial scenarios.
Properly accounting for Type II error multiplicity ensures sufficient statistical power.
These techniques are easily applicable and lead to minimal increases in sample size.