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In Silico Clinical Trials for Cardiovascular Disease
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Innovative Thinking on Endpoint Selection in Clinical Trials.

Shein-Chung Chow1, Zhipeng Huang2

  • 1Department of Biostatistics and Bioinformatics, Duke University School of Medicine , Durham , NC , USA.

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|August 28, 2019
PubMed
Summary

This study introduces a new "therapeutic index" to combine multiple clinical trial endpoints for better treatment evaluation. This approach enhances the assessment of drug safety and effectiveness, particularly in complex cancer studies.

Keywords:
Endpoint Selectioncomposite endpointtherapeutic indexutility function

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Pharmacology

Background:

  • Selecting appropriate clinical trial endpoints is crucial for evaluating treatment safety and effectiveness.
  • Multiple endpoints often exist, such as overall survival, response rate, and time to disease progression, especially in cancer trials.
  • Different endpoints can lead to varying sample size requirements and may not always correlate perfectly.

Purpose of the Study:

  • To develop an innovative endpoint, the "therapeutic index," using a utility function.
  • To combine and utilize information from all available study endpoints.
  • To improve the evaluation of treatment safety and effectiveness in clinical trials.

Main Methods:

  • Development of a novel "therapeutic index" based on a utility function.
  • Theoretical evaluation of the statistical properties and performance of the proposed index.
  • Application of the index in a numerical example from a cancer clinical trial.

Main Results:

  • The proposed therapeutic index offers a unified approach to integrating data from multiple endpoints.
  • Theoretical analysis demonstrates the statistical validity of the new endpoint.
  • The numerical example illustrates the practical utility of the therapeutic index in cancer research.

Conclusions:

  • The therapeutic index provides a valuable tool for a more comprehensive assessment of therapeutic interventions.
  • This innovative endpoint can enhance the interpretation of clinical trial results by synthesizing information from diverse measures.
  • The method is particularly relevant for complex trial designs with multiple, potentially non-translating endpoints.