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A cost-related approach for evaluating drug development programs.

Eric C Yan1, Ling Chen

  • 1Pfizer Inc. La Jolla Laboratories, 11085 Torreyana Road, San Diego, CA 92121, USA.

Statistics in Medicine
|September 3, 2004
PubMed
Summary
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This study introduces a new decision rule for drug development, using a cost function that balances development costs and potential benefits. This approach aids in making objective, data-driven decisions about continuing a drug program.

Area of Science:

  • Pharmaceutical Sciences
  • Biostatistics
  • Decision Analysis

Background:

  • Pharmaceutical statisticians primarily contribute at the clinical study level, with limited involvement in strategic drug development decisions.
  • Existing decision-making processes for drug development programs may lack quantitative objectivity, particularly concerning drug safety assessments.

Purpose of the Study:

  • To propose a novel decision rule for determining whether to continue drug development.
  • To incorporate a cost function that accounts for type I and type II errors, alongside program costs and benefits.

Main Methods:

  • Development of a cost function integrating statistical error probabilities (type I and type II) with economic factors.
  • Application of a cost-benefit analysis framework to drug development program evaluation.

Related Experiment Videos

  • Illustration of decision rule probabilities using a real-world data example.

    • Main Results:

    • The proposed cost function provides a quantitative basis for evaluating drug development programs.
    • The cost-benefit approach facilitates objective and rational decision-making for senior management.
    • Demonstrated effectiveness in a real data example, showing probabilities of correct decision-making.

    Conclusions:

    • The new decision rule enhances objective evaluation of drug development programs.
    • Incorporating cost-benefit analysis and error probabilities improves strategic decision-making in pharmaceutical development.
    • This quantitative framework supports rational progression or termination of drug candidates.