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Predicting survival using simple clinical variables: a case study in traumatic brain injury
  1. CHANTAL W P M HUKKELHOVEN,
  2. MARINUS J C EIJKEMANS,
  3. EWOUT W STEYERBERG
  1. Centre for Clinical Decision Sciences, Department of Public Health, Erasmus University Rotterdam, The Netherlands
  1. Chantal W P M Hukkelhoven, Center for Clinical Decision Sciences, Ee2073, Department of Public Health, Erasmus University Rotterdam, The Netherlands emailhukkelhoven{at}mgz.fgg.eur.nl

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Signorini et al 1developed a prognostic model to predict survival at 1 year for patients with traumatic brain injury. A strong point is that this model uses variables which are easy and cheap to measure. A thorough statistical analysis was performed, including tests for goodness of fit and checks for influential observations. The model was also validated externally in a more recent group of patients. However, during the external validation the Hosmer-Lemeshow statistic showed a significant lack of calibration (p<0.0001).

This implies that the model does not give accurate predictions of the survival of “new” patients. The lack of calibration is especially due to an overly pessimistic prediction in the patients with a poor prognosis but also to a too optimistic prediction for patients with a better prognosis (fig 2).1 This is typical for “overfitting”—that is, that a model tends to predict too extreme probabilities in new patients.

Overfitting can be limited by several procedures. One of them is that, as a rough estimate, no more than m/10 predictor degrees of freedom (df) should be analysed to construct a multiple regression model, where m is the number of events (for example, deaths).2 …

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