Development and validation of clinical prediction models: Marginal differences between logistic regression, penalized maximum likelihood estimation, and genetic programming

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@Article{Janssen2012404,
  author =       "Kristel J. M. Janssen and Ivar Siccama and 
                 Yvonne Vergouwe and Hendrik Koffijberg and T. P. A. Debray and 
                 Maarten Keijzer and Diederick E. Grobbee and 
                 Karel G. M. Moons",
  title =        "Development and validation of clinical prediction
                 models: Marginal differences between logistic
                 regression, penalized maximum likelihood estimation,
                 and genetic programming",
  journal =      "Journal of Clinical Epidemiology",
  volume =       "65",
  number =       "4",
  pages =        "404--412",
  year =         "2012",
  ISSN =         "0895-4356",
  DOI =          "doi:10.1016/j.jclinepi.2011.08.011",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0895435611002708",
  keywords =     "genetic algorithms, genetic programming, Prediction
                 model, Logistic regression, Penalised maximum
                 likelihood estimation",
  abstract =     "Objective

                 Many prediction models are developed by multivariable
                 logistic regression. However, there are several
                 alternative methods to develop prediction models. We
                 compared the accuracy of a model that predicts the
                 presence of deep venous thrombosis (DVT) when developed
                 by four different methods.

                 Study Design and Setting

                 We used the data of 2,086 primary care patients
                 suspected of DVT, which included 21 candidate
                 predictors. The cohort was split into a derivation set
                 (1,668 patients, 329 with DVT) and a validation set
                 (418 patients, 86 with DVT). Also, 100
                 cross-validations were conducted in the full cohort.
                 The models were developed by logistic regression,
                 logistic regression with shrinkage by bootstrapping
                 techniques, logistic regression with shrinkage by
                 penalised maximum likelihood estimation, and genetic
                 programming. The accuracy of the models was tested by
                 assessing discrimination and calibration.

                 Results

                 There were only marginal differences in the
                 discrimination and calibration of the models in the
                 validation set and cross-validations.

                 Conclusion

                 The accuracy measures of the models developed by the
                 four different methods were only slightly different,
                 and the 95percent confidence intervals were mostly
                 overlapped. We have shown that models with good
                 predictive accuracy are most likely developed by
                 sensible modelling strategies rather than by complex
                 development methods.",
}

Genetic Programming entries for Kristel J M Janssen Ivar Siccama Yvonne Vergouwe Hendrik Koffijberg T P A Debray Maarten Keijzer Diederick E Grobbee Karel G M Moons

Citations