Clinical research of kidney diseases IV: standard regression models

Statistical modelling is similar to the engineering concept of the study outcome being a mixture of signal and noise. For example, the signal of a model of left ventricular mass (LVM) as a function of systolic blood pressure (SBP) [1] is the average change in LVM as SBP changes (systematic component). The noise is what remains to be explained of LVM variability once the effect of SBP has been taken into account (random component). Statisticians assess the characteristics of these two elements in different ways, to establish whether a model is appropriate [2]. The present review introduces two popular families of standard regression models: generalized linear models and models for time-to-event data. The conditions that make each model appropriate are summarized along with the epidemiological meaning of its coefficients (parameters). The interested reader is referred to specific textbooks for details on model specification and assumption verification methods [3–8].