New analysis tools for observational studies

Observational studies, which are very common in rheumatology, usually follow a selected group of patients for a predetermined period of time, or infinitely, with regard to a certain outcome. Such an outcome could be a "score" reflecting an important aspect of the disease (e.g., a disease activity score), or an "event" (e.g., myocardial infarction). Rather than investigating the efficacy of a particular treatment, observational studies serve to investigate clinical associations between different (outcome) variables. Confounding, which may spuriously inflate or reduce the magnitude of a particular association, is an inherent risk in observational studies. The modern analytical approach of an observational study depends on the study question, the study design, and on how the outcome of interest has been assessed. The current article discusses several aspects of the analytical approach and requirements of the database. The focus is on longitudinal analysis, subgroup analysis, and adjustment for confounding. It is concluded that the appropriate analysis of an observational study should be a close collaboration between the clinical researcher with sufficient epidemiological knowledge and the expert statistician with sufficient interest in clinical questions