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Related Experiment Video

Updated: Mar 28, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Statistical considerations on prognostic models for glioma.

Annette M Molinaro1, Margaret R Wrensch1, Robert B Jenkins1

  • 1Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, California (A.M.M., M.R.W.); Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California (A.M.M., M.R.W.); Institute of Human Genetics, University of California San Francisco, San Francisco, California (M.R.W.); Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J.); Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (J.E.E.-P.).

Neuro-Oncology
|December 15, 2015
PubMed
Summary

Developing reliable prognostic models for glioma is crucial due to limited treatment options. This review emphasizes statistical considerations for building and validating these models to aid clinical decision-making.

Keywords:
gliomamodel buildingprognostic modelsstatisticsvalidation

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Area of Science:

  • Neuro-oncology
  • Biostatistics
  • Clinical Epidemiology

Background:

  • Glioma lacks effective treatments, necessitating prognostic models for patient care and planning.
  • Recent advances include the identification of distinct glioma subtypes.
  • Current prognostic models vary in their statistical rigor and validation.

Purpose of the Study:

  • To review statistical considerations for building and validating prognostic models in glioma research.
  • To explain existing glioma prognostic models, detailing their strengths and weaknesses.
  • To advocate for rigorous statistical methodology in prognostic model development and reporting.

Main Methods:

  • Review of statistical principles for prognostic model development: study design, model building, and validation.
  • Analysis of existing glioma prognostic models in the current literature.
  • Discussion of best practices for internal and external validation of models.

Main Results:

  • Effective prognostic models require careful study design to ensure unbiased and generalizable results.
  • Model building involves using discovery cohorts for variable selection and internal validation.
  • External validation with independent datasets is critical for assessing model performance and clinical utility.

Conclusions:

  • Adherence to robust statistical considerations in study design, model building, and validation is imperative for clinically useful glioma prognostic models.
  • Transparent reporting of methods is essential for readers to evaluate model bias and applicability.
  • Editors, reviewers, and researchers must prioritize and enforce these statistical standards.