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On responder analyses when a continuous variable is dichotomized and measurement error is present.

Michael Kunz1

  • 1Bayer Schering Pharma AG, 13342 Berlin, Germany. michael.kunz@bayerhealthcare.com

Biometrical Journal. Biometrische Zeitschrift
|January 25, 2011
PubMed
Summary

This study introduces methods to accurately estimate responder proportions in clinical trials using continuous data. It presents an unbiased ML-type estimate for improved clinical trial analysis, particularly for conditions like pre-menstrual dysphoric disorder.

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Medical Data Analysis

Background:

  • Clinical studies frequently report outcomes as responder proportions, despite continuous underlying data.
  • Defining responders based on continuous pre- and post-treatment measurements presents statistical challenges.

Purpose of the Study:

  • To develop and compare bias-corrected estimation methods for responder proportions.
  • To introduce an asymptotically unbiased maximum likelihood (ML)-type estimator.
  • To apply these methods to a clinical study of pre-menstrual dysphoric disorder (PMDD).

Main Methods:

  • Derivation and comparison of bias for different responder proportion estimators.
  • Development of an asymptotically unbiased ML-type estimator.
  • Analysis of continuous pre- and post-treatment measurements.
  • Extension to both one-sample and two-sample cases.

Main Results:

  • Bias of various estimation methods for responder proportions is quantified.
  • An asymptotically unbiased ML-type estimator is presented and shown to be effective.
  • The methods are successfully illustrated using PMDD clinical trial data.

Conclusions:

  • Accurate estimation of responder proportions from continuous data is crucial for clinical trial interpretation.
  • The proposed ML-type estimator offers an unbiased approach for analyzing responder rates.
  • This methodology enhances the statistical rigor in evaluating treatment efficacy.