Forest plots can be used to efficiently summarize the impact of covariates and other predictors on the primary and secondary parameters from population pharmacokinetic and pharmacodynamic models. They can also be used to illustrate the outcome in important patient subgroups and relate the outcome to observed or hypothetical reference subjects. Forest plots are recommended by regulatory authorities and are becoming a standard component of submission packages to the FDA and other regulatory agencies.

The goals of the workshop are:

• To get an in-depth understanding of how Forest plots should be interpreted given the variations in which point estimates and uncertainties can be derived and reference conditions can be set up. 
• To learn how to create Forest plots in R based on output from different types of models and covariate modeling techniques, implemented in NONMEM and PsN.
• To get clarity on how Forest plots depend on the underlying modeling methodology and how “Full Model-like inference” can be obtained even from modeling approaches that generate parsimonious covariate models.