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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.
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