Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

@article{Harrell1996MultivariablePM,
  title={Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.},
  author={F. Harrell and K. Lee and D. Mark},
  journal={Statistics in medicine},
  year={1996},
  volume={15 4},
  pages={
          361-87
        }
}
  • F. Harrell, K. Lee, D. Mark
  • Published 1996
  • Medicine
  • Statistics in medicine
  • Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly… CONTINUE READING
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    References

    SHOWING 1-10 OF 91 REFERENCES
    Regression modelling strategies for improved prognostic prediction.
    • 1,348
    A bootstrap resampling procedure for model building: application to the Cox regression model.
    • 539
    Applied Logistic Regression
    • 28,231
    Regression models in clinical studies: determining relationships between predictors and response.
    • 715
    Probabilistic prediction in patient management and clinical trials.
    • 284
    Measures of explained variation for survival data.
    • 166
    Predicting outcome in coronary disease. Statistical models versus expert clinicians.
    • 152
    Bootstrap investigation of the stability of a Cox regression model.
    • 307