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Multiple comparisons with two controls for ordered categorical responses.

Ping Yang1, Siu Hung Cheung1, Wai-Yin Poon1

  • 1a Department of Statistics , The Chinese University of Hong Kong , Hong Kong , China.

Journal of Biopharmaceutical Statistics
|February 17, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for clinical trials with ordered responses and two controls. It ensures accurate comparisons between treatments and controls while controlling errors and aiding sample size evaluation.

Keywords:
Familywise error ratelatent normal modelmultiple comparisonsmultiple controlsordinal responses

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Modeling

Background:

  • Ordered categorical responses are frequently encountered in clinical studies.
  • The normal latent variable model offers a framework for analyzing such data.
  • Existing methods may not adequately address scenarios with multiple controls.

Purpose of the Study:

  • To extend the normal latent variable model for clinical trials with two controls.
  • To develop a multiple comparison method that controls the familywise type I error rate.
  • To provide a procedure for sample size evaluation based on desired test power.

Main Methods:

  • Conceptualizing ordered responses as manifestations of an underlying continuous normal variable.
  • Developing a multiple comparison procedure for comparing several treatments against two controls.
  • Implementing statistical methods to control the familywise type I error rate.
  • Deriving a sample size calculation formula for a specified power level.

Main Results:

  • A novel multiple comparison method for ordinal responses with two controls is proposed.
  • The method ensures control of the familywise type I error rate.
  • A procedure for determining the necessary sample size is provided.
  • The method's utility is demonstrated through a clinical study example.

Conclusions:

  • The proposed method provides a statistically sound approach for analyzing ordinal data in clinical trials with two controls.
  • This methodology enhances the reliability of treatment efficacy comparisons.
  • The sample size evaluation procedure aids in efficient clinical trial planning.