[The application of hierarchical linear modelling for rehabilitation center comparisons in quality assurance and rehabilitation research].

For a fair comparison of rehabilitation centres with respect to the effects of the treatment provided (e. g. for the purpose of quality assurance programmes), it is essential that those factors which influence the outcome of rehabilitation treatment and over which the rehabilitation centres have no control (the so-called "confounders", such as co-morbidity and age of the patients on commencement of treatment) are included in the statistical analysis. Simple linear regression models without random effects and without interaction terms are frequently used for this purpose. However, this method has certain limitations which can be avoided if hierarchical linear modelling (HLM) is employed. HLM has the advantage over standard regression analysis methods in that it can be used to take into account the multi-level structure of a comparison problem, allows predictors to be introduced at the level of the centres and also makes it possible to model variations of regression coefficients for the centres. When the HLM technique is used, separate linear models can be produced for the various hierarchically structured data levels of the question (e. g. the levels "patients" and "centres" for rehabilitation centres, for example). Moreover, it can be empirically tested with HLMs whether the rehabilitation coefficients (e. g. effects of mean age of patients on the outcome of rehabilitation) differ significantly between the centres. In this article, we describe the use of hierarchical linear modelling on the basis of data obtained from the quality assurance programme of the statutory health insurance schemes in the field of medical rehabilitation ("QS-Reha").