Clinical outcomes are often described as events: death, stroke, epileptic seizure, multiple sclerosis lesions, recurrence of cancer, disease progression, pain, infection and bacterial/viral eradication, severe toxic adverse effect, resistance to treatment, etc. They may be quantified as time-to-event, counts of events per time interval (rates), their severity grade, or a combination of these. Such data are discrete and require specific modelling structures and methods. This article references the most common modelling approaches for categorical, count and time-to-event data, and reviews examples of such models applied in the analysis of pharmacodynamic data. Modelling is useful for identification of influential factors related to the clinical outcome, characterization and quantification of their impact, for making better informed predictions and clinical decisions, assessments of efficacy of therapeutic interventions, optimizing the individual treatments and drug development studies.