Although most statistical textbooks describe techniques for sample size calculation, it is often difficult for investigators to decide which method to use. There are many formulas available which... Show moreAlthough most statistical textbooks describe techniques for sample size calculation, it is often difficult for investigators to decide which method to use. There are many formulas available which can be applied for different types of data and study designs. However, all of these formulas should be used with caution since they are sensitive to errors, and small differences in selected parameters can lead to large differences in the sample size. In this paper, we discuss the basic principles of sample size calculations, the most common pitfalls and the reporting of these calculations. Show less
Tripepi, G.; Jager, K.J.; Dekker, F.W.; Zoccali, C. 2010
Calibration is the ability of a prognostic model to correctly estimate the probability of a given event across the whole range of prognostic estimates (for example, 30% probability of death, 40%... Show moreCalibration is the ability of a prognostic model to correctly estimate the probability of a given event across the whole range of prognostic estimates (for example, 30% probability of death, 40% probability of myocardial infarction, etc.). The key difference between calibration and discrimination is that the latter reflects the ability of a given prognostic biomarker to distinguish a status (died/survived, event/non-event), while calibration measures how much the prognostic estimation of a predictive model matches the real outcome probability (that is, the observed proportion of the event). Re-classification is another measure of prognostic accuracy and it reflects how much a new prognostic biomarker increases the proportion of individuals correctly re-classified as having or not having a given event compared to a previous classification based on an existing prognostic biomarker or predictive model. Show less
