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Seurat J, Tang Y, Mentré F, Nguyen TT. Finding optimal design in nonlinear mixed effect models using multiplicative algorithms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 207:106126. [PMID: 34038863 DOI: 10.1016/j.cmpb.2021.106126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 04/19/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVES To optimize designs for longitudinal studies analyzed by nonlinear mixed effect models (NLMEMs), the Fisher information matrix (FIM) can be used. In this work, we focused on the multiplicative algorithms, previously applied in standard individual regression, to find optimal designs for NLMEMs. METHODS We extended multiplicative algorithms to mixed models and implemented the algorithm both in R and in C. Then, we applied the algorithm to find D-optimal designs in two longitudinal data examples, one with continuous and one with binary outcome. RESULTS For these examples, we quantified the improved speed when C is used instead of R. Design optimization using the multiplicative algorithm led to designs with D-efficiency gains between 13% and 25% compared to non-optimized designs. CONCLUSION We found that the multiplicative algorithm can be used efficiently to design longitudinal studies.
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Affiliation(s)
- Jérémy Seurat
- Université de Paris, INSERM, IAME, Paris F-75006, France.
| | - Yuxin Tang
- Université de Paris, INSERM, IAME, Paris F-75006, France
| | - France Mentré
- Université de Paris, INSERM, IAME, Paris F-75006, France
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Introducing Parameter Clustering to the OED Procedure for Model Calibration of a Synthetic Inducible Promoter in S. cerevisiae. Processes (Basel) 2021. [DOI: 10.3390/pr9061053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
In recent years, synthetic gene circuits for adding new cell features have become one of the most powerful tools in biological and pharmaceutical research and development. However, because of the inherent non-linearity and noisy experimental data, the experiment-based model calibration of these synthetic parts is perceived as a laborious and time-consuming procedure. Although the optimal experimental design (OED) based on the Fisher information matrix (FIM) has been proved to be an effective means to improve the calibration efficiency, the required calculation increases dramatically with the model size (parameter number). To reduce the OED complexity without losing the calibration accuracy, this paper proposes two OED approaches with different parameter clustering methods and validates the accuracy of calibrated models with in-silico experiments. A model of an inducible synthetic promoter in S. cerevisiae is adopted for bench-marking. The comparison with the traditional off-line OED approach suggests that the OED approaches with both of the clustering methods significantly reduce the complexity of OED problems (for at least 49.0%), while slightly improving the calibration accuracy (11.8% and 19.6% lower estimation error in average for FIM-based and sensitivity-based approaches). This study implicates that for calibrating non-linear models of biological pathways, cluster-based OED could be a beneficial approach to improve the efficiency of optimal experimental design.
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Seurat J, Nguyen TT, Mentré F. Robust designs accounting for model uncertainty in longitudinal studies with binary outcomes. Stat Methods Med Res 2019; 29:934-952. [DOI: 10.1177/0962280219850588] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
To optimize designs for longitudinal studies analyzed by mixed-effect models with binary outcomes, the Fisher information matrix can be used. Optimal design approaches, however, require a priori knowledge of the model. We aim to propose, for the first time, a robust design approach accounting for model uncertainty in longitudinal trials with two treatment groups, assuming mixed-effect logistic models. To optimize designs given one model, we compute several optimality criteria based on Fisher information matrix evaluated by the new approach based on Monte-Carlo/Hamiltonian Monte-Carlo. We propose to use the DDS-optimality criterion, as it ensures a compromise between the precision of estimation of the parameters, and hence the Wald test power, and the overall precision of parameter estimation. To account for model uncertainty, we assume candidate models with their respective weights. We compute robust design across these models using compound DDS-optimality. Using the Fisher information matrix, we propose to predict the average power over these models. Evaluating this approach by clinical trial simulations, we show that the robust design is efficient across all models, allowing one to achieve good power of test. The proposed design strategy is a new and relevant approach to design longitudinal studies with binary outcomes, accounting for model uncertainty.
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Affiliation(s)
- Jérémy Seurat
- IAME, UMR 1137, INSERM, Université Paris Diderot, Paris, France
| | - Thu Thuy Nguyen
- IAME, UMR 1137, INSERM, Université Paris Diderot, Paris, France
| | - France Mentré
- IAME, UMR 1137, INSERM, Université Paris Diderot, Paris, France
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Loingeville F, Nguyen TT, Riviere MK, Mentré F. Robust designs in longitudinal studies accounting for parameter and model uncertainties – application to count data. J Biopharm Stat 2019; 30:31-45. [DOI: 10.1080/10543406.2019.1607367] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Florence Loingeville
- INSERM, IAME, UMR 1137, F-75018, Paris, France
- University Paris Diderot, Sorbonne Paris Cité, F-75018, Paris, France
- Faculty of Pharmacy, University of Lille, EA 2694, Public Health: Epidemiology and Healthcare quality, F-59000 Lille, France
| | - Thu Thuy Nguyen
- INSERM, IAME, UMR 1137, F-75018, Paris, France
- University Paris Diderot, Sorbonne Paris Cité, F-75018, Paris, France
| | - Marie-Karelle Riviere
- Statistical Methodology Group, Biostatistics and Programming department, Sanofi-Aventis R&D, Chilly-Mazarin, France
| | - France Mentré
- INSERM, IAME, UMR 1137, F-75018, Paris, France
- University Paris Diderot, Sorbonne Paris Cité, F-75018, Paris, France
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Dumont C, Lestini G, Le Nagard H, Mentré F, Comets E, Nguyen TT. PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 156:217-229. [PMID: 29428073 DOI: 10.1016/j.cmpb.2018.01.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 12/22/2017] [Accepted: 01/10/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Nonlinear mixed-effect models (NLMEMs) are increasingly used for the analysis of longitudinal studies during drug development. When designing these studies, the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. The function PFIM is the first tool for design evaluation and optimization that has been developed in R. In this article, we present an extended version, PFIM 4.0, which includes several new features. METHODS Compared with version 3.0, PFIM 4.0 includes a more complete pharmacokinetic/pharmacodynamic library of models and accommodates models including additional random effects for inter-occasion variability as well as discrete covariates. A new input method has been added to specify user-defined models through an R function. Optimization can be performed assuming some fixed parameters or some fixed sampling times. New outputs have been added regarding the FIM such as eigenvalues, conditional numbers, and the option of saving the matrix obtained after evaluation or optimization. Previously obtained results, which are summarized in a FIM, can be taken into account in evaluation or optimization of one-group protocols. This feature enables the use of PFIM for adaptive designs. The Bayesian individual FIM has been implemented, taking into account a priori distribution of random effects. Designs for maximum a posteriori Bayesian estimation of individual parameters can now be evaluated or optimized and the predicted shrinkage is also reported. It is also possible to visualize the graphs of the model and the sensitivity functions without performing evaluation or optimization. RESULTS The usefulness of these approaches and the simplicity of use of PFIM 4.0 are illustrated by two examples: (i) an example of designing a population pharmacokinetic study accounting for previous results, which highlights the advantage of adaptive designs; (ii) an example of Bayesian individual design optimization for a pharmacodynamic study, showing that the Bayesian individual FIM can be a useful tool in therapeutic drug monitoring, allowing efficient prediction of estimation precision and shrinkage for individual parameters. CONCLUSION PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in pharmacometrics and is freely available at http://www.pfim.biostat.fr.
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Affiliation(s)
- Cyrielle Dumont
- IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France; University of Lille, EA 2694, Public Health: Epidemiology and Healthcare Quality, ILIS, Lille, F-59000, France
| | - Giulia Lestini
- IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France
| | - Hervé Le Nagard
- IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France
| | - France Mentré
- IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France
| | - Emmanuelle Comets
- IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France
| | - Thu Thuy Nguyen
- IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France.
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A new method for evaluation of the Fisher information matrix for discrete mixed effect models using Monte Carlo sampling and adaptive Gaussian quadrature. Comput Stat Data Anal 2017. [DOI: 10.1016/j.csda.2016.10.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Riviere MK, Ueckert S, Mentré F. An MCMC method for the evaluation of the Fisher information matrix for non-linear mixed effect models. Biostatistics 2016; 17:737-50. [PMID: 27166250 DOI: 10.1093/biostatistics/kxw020] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 03/16/2016] [Indexed: 11/13/2022] Open
Abstract
Non-linear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal design based on the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. In recent years, estimation algorithms for NLMEMs have transitioned from linearization toward more exact higher-order methods. Optimal design, on the other hand, has mainly relied on first-order (FO) linearization to calculate the FIM. Although efficient in general, FO cannot be applied to complex non-linear models and with difficulty in studies with discrete data. We propose an approach to evaluate the expected FIM in NLMEMs for both discrete and continuous outcomes. We used Markov Chain Monte Carlo (MCMC) to integrate the derivatives of the log-likelihood over the random effects, and Monte Carlo to evaluate its expectation w.r.t. the observations. Our method was implemented in R using Stan, which efficiently draws MCMC samples and calculates partial derivatives of the log-likelihood. Evaluated on several examples, our approach showed good performance with relative standard errors (RSEs) close to those obtained by simulations. We studied the influence of the number of MC and MCMC samples and computed the uncertainty of the FIM evaluation. We also compared our approach to Adaptive Gaussian Quadrature, Laplace approximation, and FO. Our method is available in R-package MIXFIM and can be used to evaluate the FIM, its determinant with confidence intervals (CIs), and RSEs with CIs.
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Affiliation(s)
- Marie-Karelle Riviere
- INSERM, IAME, UMR 1137, F-75018 Paris, France and Univ Paris Diderot, Sorbonne Paris Cité, F-75018 Paris, France
| | - Sebastian Ueckert
- INSERM, IAME, UMR 1137, F-75018 Paris, France and Univ Paris Diderot, Sorbonne Paris Cité, F-75018 Paris, France
| | - France Mentré
- INSERM, IAME, UMR 1137, F-75018 Paris, France and Univ Paris Diderot, Sorbonne Paris Cité, F-75018 Paris, France
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Nguyen TT, Bénech H, Delaforge M, Lenuzza N. Design optimisation for pharmacokinetic modeling of a cocktail of phenotyping drugs. Pharm Stat 2015; 15:165-77. [DOI: 10.1002/pst.1731] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Indexed: 12/24/2022]
Affiliation(s)
- Thu Thuy Nguyen
- CEA, LIST; Data Analysis and Systems Intelligence Laboratory; Gif-sur-Yvette France
| | | | | | - Natacha Lenuzza
- CEA, LIST; Data Analysis and Systems Intelligence Laboratory; Gif-sur-Yvette France
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