In clinical and biomedical research, study databases are often characterized by complex relationships between the collected measurements. For example, researchers might have to deal with repeated and/or clustered measurements, or might want to analyze non-standard outcome variables like ratios, percentages, scores, or counts with a large number of zeros. Classical statistical modeling tools (like linear least squares regression) are often inadequate in these situations. For this reason, research in biostatistics is concerned with the development of novel techniques to describe and infer the relationships in „non-standard“ clinical and biomedical data. Our group has developed, among other methods, a mixed-effects model for longitudinal measurements of atrophy size (in patients with age-related macular degeneration, …) and a modeling approach for outcomes that are given by the ratio of two correlated random variables (like the amyloid-beta 42/40 ratio in dementia research, …).