The purpose of a prediction model is to estimate the chance of a particular outcome as accurately as possible (prediction). Prediction models are often developed with clinical practice in mind, and involve combining information about patients to calculate an individual’s chances of illness or recovery. The model can then be presented in the form of a clinical predictive rule. General applicability – i.e. the accuracy of the prediction model when applied to new patients in the future – is another very important aspect.

The problems which can occur when developing prediction models include the difficulty of selecting the most important predictors from a large number of variables. If this is not done carefully, the quality of the prediction model can be adversely affected. Also, the prediction model will often need to be adjusted before it can be applied to new patients. All these issues are frequently overlooked or underestimated by clinicians and researchers.

The aim of the course is to provide better knowledge and understanding of the development of prediction models that are relevant to real-life practice. We will focus on the various methods for selecting variables, and the pros and cons of these different methods. Once the prediction model has been developed, it is important to assess the quality of the prediction model. For example, we will look at whether the predictions of the model are accurate and during the course, we will also consider the various ways of measuring accuracy. The question of applying the model to new (future) patients will also be addressed. An important element of this is investigating whether the performance of the prediction model deteriorates when it is applied to new patients. This component is entitled the validation of the prediction model. We will also look at various techniques for validating the prediction model.

The course consists of an intensive programme of partly interactive lectures, combined with computer-based practical work. Examples taken from clinical practice will be used for the computer-based work.

Learning objectives

1. The participant can recognize and identify the characteristics of a prediction model. 
2. The participant can identify the weak points and strong points of the most commonly used methods for selecting variables. 
3. The participant can analyse and interpret the methods that are used to determine the quality of a prediction model (including tools for discrimination such as the ROC curve, and for calibration such as the Hosmer and Lemeshow test and a calibration curve). 
4. The participant can analyse and interpret the methods that are used to determine the value of a prediction model for real-life practice (e.g. sensitivity, specificity, positive and negative predictive abilities). 
5. The participant can convert a prediction model into a practically useful clinical instrument. 
6. The participant is familiar with the principles that play a role in internal validation such as over-fitting, optimism and shrinkage. 
7. The participant can analyse and interpret the methods used in the internal validation of prediction models, such as cross-validation and boot strapping techniques. 
8. The participant can develop prediction models, assess their quality and validate them (internally and externally) using SPSS and R software.