n most cases, if censoring isnegligible and the follow-up period clearly defined, logistic regres-sion is used; if censoring is significant or time to event is important,then a survival time approach using a Cox proportional Hazardsmodel is preferred. Other more complex model approaches such asmachine learning or competing risk models exist but are beyond thescope of this chapter [18, 19].

To ensure stability of the model coefficients in logistic and Coxregression, an event frequency of at least 10/events per degree offreedom in the model is advised [13]. For example, in a cohort of1000 patients where 100 outcomes have been observed, the pre-diction model should include at most 10 variables. Ratios of lessthan 10 events per variable can result in overfitting of the data,leading to poor generalizability in other patient cohorts. All thesegeneral aspects of study and model specification should bedescribed in the methods to allow assessment of internal validity.

Internal validity refers to the concept that a prediction model mustbe derived from the study sample in such a way that the modelcoefficients accurately reflect the true relationships between thepredictor variables and the outcome of interest. Internal validitytherefore requires that the prediction model be derived from anappropriately structured and assembled cohort.