  A prediction interval is a statistical interval that should encompass one (or
more) future observation(s) with a given coverage probability and is usually
computed based on historical control data. The application of prediction
intervals is discussed in many fields of research, such as toxicology,
pre-clinical statistics, engineering, assay validation or for the assessment of
replication studies. Anyhow, the prediction intervals implemented in predint
descent from previous work that was done in the context of toxicology and
pre-clinical applications. Hence the implemented methodology reflects the data
structures that are common in these fields of research. In toxicology the
historical data is often comprised of dichotomous or counted endpoints. Hence
it seems natural to model these kind of data based on the binomial or the
Poisson distribution. Anyhow, the historical control data is usually comprised
of several studies. These clustering gives rise to possible overdispersion
which has to be reflected for interval calculation. In pre-clinical statistics,
the endpoints are often assumed to be normal distributed, but usually are not
independent from each other due to the experimental design (cross-classified
and/or hierarchical structures). These dependencies can be modeled based on
linear random effects models. Hence, predint provides functions for the
calculation of prediction intervals and one-sided bounds for overdispersed
binomial data, for overdispersed Poisson data and for data that is modeled by
linear random effects models.
