Regression modelling strategies for improved prognostic prediction.

Journal Article (Journal Article)

Regression models such as the Cox proportional hazards model have had increasing use in modelling and estimating the prognosis of patients with a variety of diseases. Many applications involve a large number of variables to be modelled using a relatively small patient sample. Problems of overfitting and of identifying important covariates are exacerbated in analysing prognosis because the accuracy of a model is more a function of the number of events than of the sample size. We used a general index of predictive discrimination to measure the ability of a model developed on training samples of varying sizes to predict survival in an independent test sample of patients suspected of having coronary artery disease. We compared three methods of model fitting: (1) standard 'step-up' variable selection, (2) incomplete principal components regression, and (3) Cox model regression after developing clinical indices from variable clusters. We found regression using principal components to offer superior predictions in the test sample, whereas regression using indices offers easily interpretable models nearly as good as the principal components models. Standard variable selection has a number of deficiencies.

Full Text

Duke Authors

Cited Authors

  • Harrell, FE; Lee, KL; Califf, RM; Pryor, DB; Rosati, RA

Published Date

  • April 1984

Published In

Volume / Issue

  • 3 / 2

Start / End Page

  • 143 - 152

PubMed ID

  • 6463451

International Standard Serial Number (ISSN)

  • 0277-6715

Digital Object Identifier (DOI)

  • 10.1002/sim.4780030207


  • eng

Conference Location

  • England