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Related Experiment Videos

Dose finding designs for continuous responses and binary utility.

Valerii V Fedorov1, Yuehui Wu

  • 1GlaxoSmithKline, Collegeville, Pennsylvania, USA.

Journal of Biopharmaceutical Statistics
|November 21, 2007
PubMed
Summary
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Dichotomizing continuous clinical trial responses for drug approval, while common, significantly reduces parameter precision and statistical power. This study explores utility functions and optimal designs to mitigate these losses.

Area of Science:

  • Clinical Trials
  • Biostatistics
  • Drug Development

Background:

  • Clinical trials often involve continuous responses, but regulatory approval hinges on dichotomized efficacy and toxicity thresholds.
  • A utility function based on these dichotomized responses is crucial for decision-making.

Purpose of the Study:

  • To evaluate the impact of dichotomizing continuous responses on parameter estimation precision and statistical power in clinical trials.
  • To explore utility functions and optimal designs for handling dichotomized responses in drug development.

Main Methods:

  • Utilized normally distributed correlated responses and a probit transform to build a utility function.
  • Employed locally optimal designs as benchmarks for composite and adaptive designs.
  • Focused on D-criterion for dose-response models and two-stage designs.

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Main Results:

  • Dichotomizing continuous responses leads to a substantial loss in the precision of estimated parameters.
  • This practice also results in a significant loss of statistical power.

Conclusions:

  • Reporting dichotomized responses in clinical trials negatively impacts the reliability of parameter estimates.
  • Optimized designs and utility functions are necessary to address the precision and power loss associated with dichotomization.