V² ACHER: Visualization of complex trial data in pharmacometric analyses with covariates.

ABSTRACT: Pharmacometric models can enhance clinical decision making, with covariates exposing potential contributions to variability of subpopulation characteristics, for example, demographics or disease status. Intuitive visualization of models with multiple covariates is needed because sparsity of data in visualizations trellised by covariate values can raise concerns about the credibility of the underlying model. V² ACHER, introduced here, is a stepwise transformation of data that can be applied to a variety of static (non-ordinary-differential-equation-based) pharmacometric analyses. This work uses four examples of increasing complexity to show how the transformation elucidates the relationship between observations and model results and how it can also be used in visual predictive ch

SUBMITTER: Lommerse J 

PROVIDER: S-EPMC8452296 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature