Disease Models Introduction
Many medical conditions progress over time, such as cancer, atherosclerosis or mental illness. This progression may follow complex patterns and be very different for different people. Disease progression models aim to capture the natural pattern of a disease, in terms of measurable endpoints, and to understand both the degree of variability and which covariates influence progression, in particular pharmaceutical interventions.
The natural progression of a disease can sometimes be described parametrically, however there is often unexplained random variations that requires time series models for autocorrelation. Both natural disease progression and the impact of drugs on this underlying progress tend to occur on a slower timescale than pharmacokinetics or mechanism-of-action biomarkers, thus integrated measures of drug exposure are often used. However, finding the optimal link between the drug profile and the disease biology is often a fascinating challenge, requiring careful thought and often the creation of novel models.