Statistical modelling techniques should not be confused with formats for presentation

Adams and Leveson describe ‘five main methods to develop clinical prediction models: scoring systems derived from univariate analysis; multivariable regression analysis; nomograms; neural networks and CART analysis’.1 This list is a mix of statistical techniques to develop models and formats to present models for the user. We believe that such a list can be confusing rather than help doctors to become familiar with decision making tools. Model development and model presentation are two separate steps. First an adequate statistical technique needs to be chosen for model development. Given the developed model, an easy to use format can be chosen.2
Univariate and multivariable regression modelling are statistical techniques to assess strengths of predictive effects. Multivariable models are preferrable, since correlation between predictors is considered. Neural networks and CART analyses are alternatives for regression analysis. The large disadvantage of the latter two is that the derived rules may be good at describing the data used for development and but perform often poorly in new patients.3 Once the model is developed with the chosen statistical technique, the analyst may present the model as a scoring system, nomogram, or decision tree. The format should be based on the intended application. Decision trees can be produced by CART analysis, but have the disadvantage of a limited number of predicted risks, as mentioned by the authors.
Prediction models may be underused in clinical practice because of inappropriate model development (e.g. in small datasets with relatively many predictors and poor handling of missing data), lack of validation, and no analysis for impact. Prediction modelling is an area of expertise in itself. The upcoming guidelines for the reporting of risk prediction models under the wings of the EQUATOR network (http://www.equator-network.org) will be an important step forward in prediction research.

References
1 Adams ST, Leveson SH. Clinical prediction rules. BMJ 2012;344:d8312 (16 January).

2. Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York, NY: Springer Publishing Company; 2009

3. Tu JV. Advantages and Disadvantages of Using Artificial Neural Networks versus Logistic Regression for Predicting Medical Outcomes. J Clin Epidemiol 1996; 49: 1225-31.

Competing interests: None declared