Developing Reliable Prognostic Models

Although prognostic models can be of great clinical assistance, few prognostic models are in common use (not just in cancer) (6). As Wyatt and Altman (3) observed: "However accurate a model is in statistical terms, doctors will be reluctant to use it to inform their patient management decisions unless they believe in the model and its predictions." They suggested the following conditions for clinical credibility: (i) All clinically relevant patient data should have been tested for inclusion in the model; (ii) it should be simple for doctors to obtain all the patient data required, reliably and without expending undue resources, in time to generate the prediction and guide decisions; (iii) model builders should try to avoid arbitrary thresholds for continuous variables; (iv) the model's structure should be apparent and its predictions should make sense to the doctors who will rely on them; and (v) it should be simple for doctors to calculate the model's prediction for a patient. The difficulties of developing reliable multiple regression models have been much discussed (7-10). Both clinical and statistical aspects are critical.

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