Development of visual predictive checks accounting for multimodal parameter distributions in mixture models.

ABSTRACT: The assumption of interindividual variability being unimodally distributed in nonlinear mixed effects models does not hold when the population under study displays multimodal parameter distributions. Mixture models allow the identification of parameters characteristic to a subpopulation by describing these multimodalities. Visual predictive check (VPC) is a standard simulation based diagnostic tool, but not yet adapted to account for multimodal parameter distributions. Mixture model analysis provides the probability for an individual to belong to a subpopulation (IP_mix) and the most likely subpopulation for an individual to belong to (MIXEST). Using simulated data examples, two implementation strategies were followed to split the data into subpopulations for the development of m

SUBMITTER: Arshad U 

PROVIDER: S-EPMC6560505 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature