Open AccessV 2 ACHER: Visualization of complex trial data in pharmacometric analyses with covariates
Author(s) -
Lommerse Jos,
Plock Nele,
Cheung S. Y. Amy,
Sachs Jeffrey R.
Publication year - 2021
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12679
Subject(s) - covariate , visualization , computer science , credibility , data visualization , data mining , set (abstract data type) , demographics , econometrics , data science , machine learning , mathematics , programming language , political science , law , demography , sociology
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. V 2 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 checks to confirm the quality of a model. V 2 ACHER facilitates consistent, intuitive, single‐plot visualization of a multicovariate model with a complex data set, thereby enabling easier model communication for modelers and for cross‐functional development teams and facilitating confident use in support of decisions.