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Discovering, comparing, and combining moderators of treatment on outcome after randomized clinical trials: a

Helena Chmura Kraemer1

  • 1Department of Psychiatry and Behavioral Sciences, Stanford University, 1116 Forest Avenue, Palo Alto, CA 94301-3032, USA. hckhome@pacbell.net

Statistics in Medicine
|January 11, 2013
PubMed

Insights

Personalized medicine research is shifting focus to effect sizes and moderators. This study defines moderator effect size to guide clinicians in prescribing tailored treatments for better patient outcomes.

Area of Science:

  • Clinical Psychology
  • Biostatistics
  • Medical Research

Background:

  • Treatment effectiveness varies significantly among individuals with a disorder.
  • Many treatments exhibit small effect sizes, necessitating a move towards personalized approaches.
  • Randomized clinical trials are increasingly emphasizing effect sizes and moderator identification.

Purpose of the Study:

  • To define and quantify moderator effect size in clinical research.
  • To enable comparison and selection of the most effective moderators.
  • To develop composite moderators for enhanced treatment personalization.

Main Methods:

  • Focus on parametric assumptions for defining moderator strength.
  • Developing methods to assess moderator effect size for clinical significance.
  • Utilizing randomized clinical trials to identify treatment effect moderators.

Main Results:

  • The study provides a framework for defining and measuring moderator effect size.
  • This quantification allows for the comparison of different moderators.
  • The approach facilitates the development of composite moderators.

Conclusions:

  • Quantifying moderator effect size is crucial for advancing personalized medicine.
  • This methodology aids in identifying which treatments work best for specific patient subgroups.
  • The findings offer guidance for clinicians to optimize treatment selection and improve patient outcomes.

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