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Methods to evaluate risks for composite end points and their individual components.

Robert J Glynn1, Bernard Rosner

  • 1Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health, 900 Commonwealth Avenue East, Boston, MA 02215-1204, USA. rglynn@rics.bwh.harvard.edu

Journal of Clinical Epidemiology
|May 6, 2004
PubMed
Summary
This summary is machine-generated.

Evaluating composite end points in studies is crucial. This research developed a strategy to assess risk factor heterogeneity, finding that a uniform effects model performed comparably to complex ones for composite cardiovascular disease outcomes.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Clinical Research

Background:

  • Composite end points are frequently used in both randomized and observational studies.
  • Limited literature exists on developing and critiquing models for composite end points.

Purpose of the Study:

  • To examine methods for evaluating heterogeneity in risk factor effects across components of a composite end point.
  • To determine how heterogeneity impacts the prediction of the composite end point.

Main Methods:

  • Utilized the composite cardiovascular disease end point from the Physicians' Health Study (n=16,688).
  • Compared polytomous logistic regression models with varying risk factor effects against a logistic model with common effects.
  • Assessed heterogeneity using likelihood ratio tests and model performance via ROC curves.

Main Results:

  • Identified heterogeneity in the effects of age, alcohol, and diabetes across composite end point components.
  • A model assuming uniform effects explained over 90% of the log-likelihood change in the best polytomous model.
  • Both uniform and polytomous models demonstrated similar performance based on ROC curve comparisons.

Conclusions:

  • The proposed strategy aids in evaluating the validity of composite end point analyses.
  • This approach can effectively identify heterogeneity in risk factor effects for composite outcomes.