Clinical research of kidney diseases V: extended analytic models

In some study designs the same epidaemiological unit is observed more than once. For example, in a cross-sectional study of the radial artery flow rate, several outcome values can be recorded on the same subject under different experimental conditions (e.g. exposure to different vasoactive substances). Some longitudinal studies typically monitor participants over time and both predictors (e.g. blood pressure) and outcomes (e.g. left ventricular mass index) are measured on different occasions in the same subject. In other designs, observations can fall into groups ( clustered data ), such as single measurements taken on a paired organ (e.g. the eye or the kidney) or single observations on different members of the same hospital/region or family. More complex designs may lead to a combination of clustering and repeated/longitudinal measurements ( Table 1 ). All these designs generate correlated outcome data ( Figure 1 ). ... Multiple measurements on the same subjects are correlated because their values tend to be closer to each other than those obtained from different individuals. Similarly, single assessments of paired organs or members of the same hospital/region or family are correlated because different organs of the same subject and different individuals of the same community share biologic experiences, environmental exposures and genetic background. For some outcomes such as disease recurrence, previous experience may induce negative correlation. In all cases, once a measurement value has been obtained further values within the same individual/cluster can be more accurately ‘guessed’ and the corresponding measurement errors are no longer due to chance alone. In these situations traditional regression methods are inappropriate as they assume independent errors [ 1,2 ]. Two major analytical approaches exist for the analysis of correlated generalized linear (continuous, binary and count outcomes) and time-to-event data: random effect modelling and variance-corrected methods. The main assumption of these approaches is that the responses are correlated within cluster/subject, but independent between clusters/subjects.