A joint analysis proposal of nonlinear longitudinal and time-to-event right-, interval-censored data for modeling pregnancy miscarriage.

Pregnancy in-vitro fertilization (IVF) cases are associated with adverse first-trimester outcomes in comparison to spontaneously achieved pregnancies. Human chorionic gonadotrophin β subunit (β-HCG) is a well-known biomarker for the diagnosis and monitoring of pregnancy after IVF. Low levels of β-HCG during this period are related to miscarriage, ectopic pregnancy, and IVF procedure failures.

Design of a pharmacokinetic/pharmacodynamic model for administration of low dose peripheral norepinephrine during general anaesthesia.

Aims: Intraoperative hypotension is a risk factor for kidney, heart and cognitive postoperative complications. Literature suggests that the use of low-dose peripheral norepinephrine (NOR) reduces organ dysfunction, yet its administration remains unstandardized. In this work we develop a pharmacokinetic (PK)/pharmacodynamic (PD) model of NOR and its effect on mean arterial pressure (MAP).

An ensemble model based on early predictors to forecast COVID-19 health care demand in France.

Short-term forecasting of the COVID-19 pandemic is required to facilitate the planning of COVID-19 health care demand in hospitals. Here, we evaluate the performance of 12 individual models and 19 predictors to anticipate French COVID-19-related health care needs from September 7, 2020, to March 6, 2021. We then build an ensemble model by combining the individual forecasts and retrospectively test this model from March 7, 2021, to July 6, 2021.

SAMBA: A novel method for fast automatic model building in nonlinear mixed-effects models.

The success of correctly identifying all the components of a nonlinear mixed-effects model is far from straightforward: it is a question of finding the best structural model, determining the type of relationship between covariates and individual parameters, detecting possible correlations between random effects, or also modeling residual errors. We present the Stochastic Approximation for Model Building Algorithm (SAMBA) procedure and show how this algorithm can be used to speed up this process of model building by identifying at each step how best to improve some of the model components. The principle of this algorithm basically consists in "learning something" about the "best model," even when a "poor model" is used to fit the data.

Reciprocal association between voting and the epidemic spread of COVID-19: observational and dynamic modeling study.

Background: Whether voting is a risk factor for epidemic spread is unknown. Reciprocally, whether an epidemic can deter citizens from voting has not been often studied. We aimed to investigate such relationships for France during the coronavirus disease 19 (COVID-19) epidemic.

Predicting the propagation of COVID-19 at an international scale: extension of an SIR model.

Objectives: Several epidemiological models have been published to forecast the spread of the COVID-19 pandemic, yet many of them have proven inaccurate for reasons that remain to be fully determined. We aimed to develop a novel model and implement it in a freely accessible web application.

Design: We built an SIR-type compartmental model with two additional compartments: (deceased patients); (individuals who will die but who will not infect anybody due to social or medical isolation) and integration of a time-dependent transmission rate and a periodical weekly component linked to the way in which cases and deaths are reported.

SPIX: A new software package to reveal chemical reactions at trace amounts in very complex mixtures from high-resolution mass spectra dataset.

Rationale: High-resolution mass spectrometry based non-targeted screening has a huge potential for applications in environmental sciences, engineering and regulation. However, it produces large datasets for which full appropriate processing is a real challenge; the development of processing software is the last building-block to enable large-scale use of this approach.

Methods: A new software application, SPIX, has been developed to extract relevant information from high-resolution mass spectral datasets.

A mathematical approach to deal with nanoparticle polydispersity in surface enhanced Raman spectroscopy to quantify antineoplastic agents.

Antineoplastic agents are, for most of them, highly toxic drugs prepared at hospital following individualized prescription. To protect patients and healthcare workers, it is important to develop analytical tools able to identify and quantify such drugs on a wide concentration range. In this context, surface enhanced Raman spectroscopy (SERS) has been tested as a specific and sensitive technique.

Inheritance and variability of kinetic gene expression parameters in microbial cells: modeling and inference from lineage tree data.

Motivation: Modern experimental technologies enable monitoring of gene expression dynamics in individual cells and quantification of its variability in isogenic microbial populations. Among the sources of this variability is the randomness that affects inheritance of gene expression factors at cell division. Known parental relationships among individually observed cells provide invaluable information for the characterization of this extrinsic source of gene expression noise.

Mathematical model to predict B-type natriuretic peptide levels in haemodialysis patients.

Aim: Clinical interpretation of B-type natriuretic peptide (BNP) levels in haemodialysis (HD) patients for fluid management remains elusive.

Methods: We conducted a retrospective observational monocentric study. We built a mathematical model to predict BNP levels, using multiple linear regressions.

Pharmacometrics models with hidden Markovian dynamics.

The aim of this paper is to provide an overview of pharmacometric models that involve some latent process with Markovian dynamics. Such models include hidden Markov models which may be useful for describing the dynamics of a disease state that jumps from one state to another at discrete times. On the contrary, diffusion models are continuous-time and continuous-state Markov models that are relevant for modelling non observed phenomena that fluctuate continuously and randomly over time.

Enhanced Method for Diagnosing Pharmacometric Models: Random Sampling from Conditional Distributions.

Purpose: For nonlinear mixed-effects pharmacometric models, diagnostic approaches often rely on individual parameters, also called empirical Bayes estimates (EBEs), estimated through maximizing conditional distributions. When individual data are sparse, the distribution of EBEs can "shrink" towards the same population value, and as a direct consequence, resulting diagnostics can be misleading.

Methods: Instead of maximizing each individual conditional distribution of individual parameters, we propose to randomly sample them in order to obtain values better spread out over the marginal distribution of individual parameters.

What do we mean by identifiability in mixed effects models?

We discuss the question of model identifiability within the context of nonlinear mixed effects models. Although there has been extensive research in the area of fixed effects models, much less attention has been paid to random effects models. In this context we distinguish between theoretical identifiability, in which different parameter values lead to non-identical probability distributions, structural identifiability which concerns the algebraic properties of the structural model, and practical identifiability, whereby the model may be theoretically identifiable but the design of the experiment may make parameter estimation difficult and imprecise.

The recruitment of p47(phox) and Rac2G12V at the phagosome is transient and phosphatidylserine dependent.

Background Information: During phagocytosis, neutrophils internalise pathogens in a phagosome and produce reactive oxygen species (ROS) by the NADPH oxidase to kill the pathogen. The cytosolic NADPH oxidase subunits p40(phox), p47(phox), p67(phox) and Rac2 translocate to the phagosomal membrane to participate in enzyme activation. The kinetics of this recruitment and the underlying signalling pathways are only partially understood.

Analysis of exposure-response of CI-945 in patients with epilepsy: application of novel mixed hidden Markov modeling methodology.

We propose to describe exposure-response relationship of an antiepileptic agent, using mixed hidden Markov modeling methodology, to reveal additional insights in the mode of the drug action which the novel approach offers. Daily seizure frequency data from six clinical studies including patients who received gabapentin were available for the analysis. In the model, seizure frequencies are governed by underlying unobserved disease activity states.

Automatic data binning for improved visual diagnosis of pharmacometric models.

Visual Predictive Checks (VPC) are graphical tools to help decide whether a given model could have plausibly generated a given set of real data. Typically, time-course data is binned into time intervals, then statistics are calculated on the real data and data simulated from the model, and represented graphically for each interval. Poor selection of bins can easily lead to incorrect model diagnosis.

Model-based analyses of bioequivalence crossover trials using the stochastic approximation expectation maximisation algorithm.

In this work, we develop a bioequivalence analysis using nonlinear mixed effects models (NLMEM) that mimics the standard noncompartmental analysis (NCA). We estimate NLMEM parameters, including between-subject and within-subject variability and treatment, period and sequence effects. We explain how to perform a Wald test on a secondary parameter, and we propose an extension of the likelihood ratio test for bioequivalence.

The use of the SAEM algorithm in MONOLIX software for estimation of population pharmacokinetic-pharmacodynamic-viral dynamics parameters of maraviroc in asymptomatic HIV subjects.

Using simulated viral load data for a given maraviroc monotherapy study design, the feasibility of different algorithms to perform parameter estimation for a pharmacokinetic-pharmacodynamic-viral dynamics (PKPD-VD) model was assessed. The assessed algorithms are the first-order conditional estimation method with interaction (FOCEI) implemented in NONMEM VI and the SAEM algorithm implemented in MONOLIX version 2.4.

Implementation and evaluation of the SAEM algorithm for longitudinal ordered categorical data with an illustration in pharmacokinetics-pharmacodynamics.

Analysis of longitudinal ordered categorical efficacy or safety data in clinical trials using mixed models is increasingly performed. However, algorithms available for maximum likelihood estimation using an approximation of the likelihood integral, including LAPLACE approach, may give rise to biased parameter estimates. The SAEM algorithm is an efficient and powerful tool in the analysis of continuous/count mixed models.

Maximum likelihood estimation of long-term HIV dynamic models and antiviral response.

HIV dynamics studies, based on differential equations, have significantly improved the knowledge on HIV infection. While first studies used simplified short-term dynamic models, recent works considered more complex long-term models combined with a global analysis of whole patient data based on nonlinear mixed models, increasing the accuracy of the HIV dynamic analysis. However statistical issues remain, given the complexity of the problem.

Statistical aspects of environmental risk assessment of GM plants for effects on non-target organisms.

Previous European guidance for environmental risk assessment of genetically modified plants emphasized the concepts of statistical power but provided no explicit requirements for the provision of statistical power analyses. Similarly, whilst the need for good experimental designs was stressed, no minimum guidelines were set for replication or sample sizes. Furthermore, although substantial equivalence was stressed as central to risk assessment, no means of quantification of this concept was given.