Robust designs in longitudinal studies ...
Type de document :
Article dans une revue scientifique
PMID :
URL permanente :
Titre :
Robust designs in longitudinal studies accounting for parameter and model uncertainties -Application to count data
Auteur(s) :
Loingeville, Florence [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Nguyen, Thu Thuy [Auteur]
Riviere, Marie-Karelle [Auteur]
Mentré, France [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Nguyen, Thu Thuy [Auteur]
Riviere, Marie-Karelle [Auteur]
Mentré, France [Auteur]
Titre de la revue :
Journal of Biopharmaceutical Statistics
Nom court de la revue :
J Biopharm Stat
Numéro :
27
Pagination :
1-15
Éditeur :
Taylor & Francis
Date de publication :
2019-04-27
ISSN :
1054-3406
Mot(s)-clé(s) :
Fisher information matrix
Longitudinal count data
Markov Chain Hamiltonian Monte Carlo
Nonlinear mixed effect models
Optimal design
Robust design
Longitudinal count data
Markov Chain Hamiltonian Monte Carlo
Nonlinear mixed effect models
Optimal design
Robust design
Discipline(s) HAL :
Sciences du Vivant [q-bio]/Sciences pharmaceutiques
Résumé en anglais : [en]
Nonlinear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal designs based on the expected Fisher information matrix (FIM) can be used. A method evaluating ...
Lire la suite >Nonlinear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal designs based on the expected Fisher information matrix (FIM) can be used. A method evaluating the FIM using Monte- Carlo Hamiltonian Monte-Carlo (MC-HMC) has been proposed and implemented in the R package MIXFIM using Stan. This approach, however, requires a priori knowledge of models and parameters, which leads to locally optimal designs. The objective of this work was to extend this MC-HMC-based method to evaluate the FIM in NLMEMs accounting for uncertainty in parameters and in models. When introducing uncertainty in the population parameters, we evaluated the robust FIM as the expectation of the FIM computed by MC-HMC over the distribution of these parameters. Then, the compound D-optimality criterion (CD optimality), corresponding to a weighted product of the D-optimality criteria of several candidate models, was used to find a common CD-optimal design for the set of candidate models. Finally, a compound DE-criterion (CDE optimality), corresponding to a weighted product of the normalized determinants of the robust FIMs of all the candidate models accounting for uncertainty in parameters, was calculated to find the CDE-optimal design which was robust on both parameters and model. These methods were applied in a longitudinal Poisson count model. We assumed prior distributions on the population parameters as well as several candidate models describing the relationship between the logarithm of the event rate parameter and the dose. We found that assuming uncertainty in parameters could lead to different optimal designs, and misspecification of models could induce designs with low efficiencies. The CD- or CDE-optimal designs therefore provided a good compromise for different candidate models. Finally, the proposed approach allows for the first time optimization of designs for repeated discrete data accounting for parameter and model uncertainties.Lire moins >
Lire la suite >Nonlinear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal designs based on the expected Fisher information matrix (FIM) can be used. A method evaluating the FIM using Monte- Carlo Hamiltonian Monte-Carlo (MC-HMC) has been proposed and implemented in the R package MIXFIM using Stan. This approach, however, requires a priori knowledge of models and parameters, which leads to locally optimal designs. The objective of this work was to extend this MC-HMC-based method to evaluate the FIM in NLMEMs accounting for uncertainty in parameters and in models. When introducing uncertainty in the population parameters, we evaluated the robust FIM as the expectation of the FIM computed by MC-HMC over the distribution of these parameters. Then, the compound D-optimality criterion (CD optimality), corresponding to a weighted product of the D-optimality criteria of several candidate models, was used to find a common CD-optimal design for the set of candidate models. Finally, a compound DE-criterion (CDE optimality), corresponding to a weighted product of the normalized determinants of the robust FIMs of all the candidate models accounting for uncertainty in parameters, was calculated to find the CDE-optimal design which was robust on both parameters and model. These methods were applied in a longitudinal Poisson count model. We assumed prior distributions on the population parameters as well as several candidate models describing the relationship between the logarithm of the event rate parameter and the dose. We found that assuming uncertainty in parameters could lead to different optimal designs, and misspecification of models could induce designs with low efficiencies. The CD- or CDE-optimal designs therefore provided a good compromise for different candidate models. Finally, the proposed approach allows for the first time optimization of designs for repeated discrete data accounting for parameter and model uncertainties.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Projet Européen :
Établissement(s) :
CHU Lille
Université de Lille
Université de Lille
Date de dépôt :
2020-06-08T14:11:49Z