Validation of Clinical Prediction Models: Theory and Applications in Testicular Germ Cell Cancer

linical prediction models combine patient characteristics to predict the probability of having a certain disease (diagnosis) or the probability that a particular disease state will occur (prognosis). The predicted probability of the diagnostic or prognostic outcome may assist the clinician in decision making for patient care. Before a prediction model can reliably be applied in clinical practice, the performance of the model in new patients needs to be studied (‘external validity’). This thesis described several theoretical and practical aspects of the external validation of clinical prediction models. The objectives were (i) to describe aspects of model validity and relevant performance measures; (ii) to estimate the power of these performance measures; (iii) to externally validate a prediction model for residual mass histology in testicular cancer; and (iv) to update this model with all available information. Three aspects of model performance are discussed: calibration, discrimination, and clinical usefulness. The external validity of a model does not only depend on the new patients for whom the model is applied, but also on the development process of the model. Therefore, this thesis contained also some illustrations of model development aspects. The development process of the prediction model for residual mass histology in nonseminomatous testicular germ cell cancer was described; the model predicts the probability that a residual retroperitoneal mass contains only benign tissue after cis-platin based chemotherapy for metastatic tumour.

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