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[Subgroup analysis and forest plots: Limitations and interests].

Thomas Filleron1, Julia Gilhodes1, Jean-Pierre Delord2

  • 1Institut Claudius-Regaud, IUCT-oncopole, bureau des essais cliniques, 1, avenue Irène-Joliot-Curie, 31059 Toulouse, France.

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|November 9, 2016
PubMed
Summary

Subgroup analyses in clinical trials help personalize medicine by assessing treatment effects in specific patient groups. Proper planning and interpretation are crucial for reliable results and guiding treatment strategies.

Keywords:
Analyse en sous-groupeClinical trialsEssais cliniquesHeterogeneityHétérogénéitéMultiplicity of testMultiplicité des testsSubgroup analysis

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Area of Science:

  • Clinical Trials
  • Personalized Medicine
  • Biostatistics

Background:

  • Treatment effects vary across patient subpopulations, necessitating subgroup analyses.
  • Existing literature reviews indicate inconsistencies in the analysis and interpretation of subgroup data.
  • Subgroup analyses present methodological challenges, including inflated alpha risk and reduced statistical power.

Purpose of the Study:

  • To present methodological principles for planning subgroup analyses in clinical trials.
  • To provide recommendations for the correct interpretation of subgroup analysis results.
  • To enhance the reliability and utility of subgroup analyses in personalized medicine.

Main Methods:

  • Review of methodological principles for subgroup analysis planning.
  • Discussion of common statistical challenges in subgroup analysis.
  • Development of guidelines for consistent interpretation of results.

Main Results:

  • Subgroup analyses, when properly conducted, offer valuable insights into treatment efficacy in subpopulations.
  • Methodological inconsistencies and statistical issues can compromise the validity of subgroup findings.
  • Clear guidelines are needed to ensure accurate interpretation and application of subgroup data.

Conclusions:

  • Adherence to methodological principles is essential for robust subgroup analyses.
  • Standardized interpretation frameworks can improve the clinical utility of subgroup findings.
  • Effective subgroup analysis supports the advancement of personalized medicine strategies.