Inference in Gaussian state-space models with mixed effects for multiple epidemic dynamics.

The estimation from available data of parameters governing epidemics is a major challenge. In addition to usual issues (data often incomplete and noisy), epidemics of the same nature may be observed in several places or over different periods. The resulting possible inter-epidemic variability is rarely explicitly considered. Here, we propose to tackle multiple epidemics through a unique model incorporating a stochastic representation for each epidemic and to jointly estimate its parameters from noisy and partial observations. By building on a previous work for prevalence data, a Gaussian state-space model is extended to a model with mixed effects on the parameters describing simultaneously several epidemics and their observation process. An appropriate inference method is developed, by coupling the SAEM algorithm with Kalman-type filtering. Moreover, we consider here incidence data, which requires to develop a new version of the filtering algorithm. Its performances are investigated on SIR simulated epidemics for prevalence and incidence data. Our method outperforms an inference method separately processing each dataset. An application to SEIR influenza outbreaks in France over several years using incidence data is also carried out. Parameter estimations highlight a non-negligible variability between influenza seasons, both in transmission and case reporting. The main contribution of our study is to rigorously and explicitly account for the inter-epidemic variability between multiple outbreaks, both from the viewpoint of modeling and inference with a parsimonious statistical model.

A novel modelling approach to quantify the response of dairy goats to a high-concentrate diet.

High-producing ruminants need high-concentrate diets to satisfy their nutrient requirements and meet performance objectives. However, such diets induce sub-acute ruminal acidosis (SARA), which will adversely affect dry matter intake and lead to lower production performance. This work develops a novel modelling approach to quantify the capacity of dairy goats to adapt to a high-concentrate diet challenge at the individual level. The animal model used was dairy goats (from Saanen or Alpine breed), and rumen pH was used as the indicator of the response. A three-step modelling procedure was developed to quantify daily scores and produce a single global index for animals' adaptive response to the new diet. The first step summarizes the post-prandial kinetics of rumen acid status using three synthetic variables. In the second step, the effect of time on the response of goats is described, in the short and long terms. In the last step, a metric based on phase trajectories ranks goats for their resilience capacity. This modelling procedure showed a high variability among the goats in response to the new diet, highlighting in particular their daily and general strategies to buffer the effect of the diet change. Two main categories of adaptive strategies were observed: (i) acid status increased, but the goats tried to minimize its variations, and (ii) acid status oscillated between increases and decreases. Such phenotyping, alongside other behavioral, digestive, and metabolic measures, can help to determine biomarkers of goats' capacity to adapt to diets of higher nutritive value and to increase production performance without compromising their health status. Quantifying the capacity of goats to buffer the effect of highly fermentable diets helps to better adapt feed to animals in precision livestock farming. This procedure is generic and can be adapted to any indicator of animal health and performance. In particular, several indicators can be combined to assess multi-performance, which is of major interest in the context of selection for robust animals.

An iterative algorithm for joint covariate and random effect selection in mixed effects models.

We consider joint selection of fixed and random effects in general mixed-effects models. The interpretation of estimated mixed-effects models is challenging since changing the structure of one set of effects can lead to different choices of important covariates in the model. We propose a stepwise selection algorithm to perform simultaneous selection of the fixed and random effects. It is based on Bayesian Information criteria whose penalties are adapted to mixed-effects models. The proposed procedure performs model selection in both linear and nonlinear models. It should be used in the low-dimension setting where the number of ovariates and the number of random effects are moderate with respect to the total number of observations. The performance of the algorithm is assessed via a simulation study, which includes also a comparative study with alternatives when available in the literature. The use of the method is illustrated in the clinical study of an antibiotic agent kinetics.

Patterns of amino acid intake are strongly associated with cardiovascular mortality, independently of the sources of protein.

BACKGROUND: The intake of specific amino acids (AA) has been associated with cardiovascular health, but amino acids are consumed together as dietary protein. Here we investigated the association between identified patterns of amino acid intake and cardiovascular mortality. METHODS: A total of 2216 cardiovascular deaths among 79 838 men and women from the Adventist Health Study-2 were included in our analysis. Baseline dietary patterns based on the participants' amino acids intakes were derived by factor analysis. Using Cox regression analyses, we estimated multivariate-adjusted hazard ratios (HRs) adjusted for sociodemographic and lifestyle factors and other dietary components. RESULTS: Three patterns of amino acids were identified. Factor 1 was positively associated with cardiovascular disease (CVD) mortality [hazard ratio (HR)Q5-Q1: 1.62, 98.75% confidence interval (CI): 1.15, 2.28; P-trend <0.001]; and Factors 2 and 3 were inversely associated with CVD mortality (HR Q5-Q1 Factor 2: 0.74, 98.75% CI: 0.53, 1.04; P-trend <0.01 and HR Q5-Q1 Factor 3: 0.65, 98.75% CI: 0.44, 0.95; P-trend <0.05]. The associations with Factor 1 (with high loadings on indispensable amino acids such as branched chain amino acids, lysine, methionine) and Factor 3 (with high loadings on non-indispensable amino acids, namely arginine, glycine, aspartate+asparagine) remained significant after further adjustment for nutrient intake and for the five protein source patterns identified previously (HR Q5-Q1: 1.56 (0.99, 2.45) and 0.55 (0.35, 0.85); P-trends < 0.01). CONCLUSIONS: Indispensable AA have a positive and some non-indispensable AA have a negative, independent, strong association with the risk of cardiovascular mortality.

Patterns of plant and animal protein intake are strongly associated with cardiovascular mortality: the Adventist Health Study-2 cohort.

Background: Current evidence suggests that plant and animal proteins are intimately associated with specific large nutrient clusters that may explain part of their complex relation with cardiovascular health. We aimed at evaluating the association between specific patterns of protein intake with cardiovascular mortality. Methods: We selected 81 337 men and women from the Adventist Health Study-2. Diet was assessed between 2002 and 2007, by using a validated food frequency questionnaire. Dietary patterns based on the participants' protein consumption were derived by factor analysis. Cox regression analysis was used to estimate multivariate-adjusted hazard ratios (HRs) adjusted for sociodemographic and lifestyle factors and dietary components. Results: There were 2276 cardiovascular deaths during a mean follow-up time of 9.4 years. The HRs for cardiovascular mortality were 1.61 [98.75% confidence interval (CI), 1.12 2.32; P-trend < 0.001] for the 'Meat' protein factor and 0.60 (98.75% CI, 0.42 0.86; P-trend < 0.001) for the 'Nuts & Seeds' protein factor (highest vs lowest quintile of factor scores). No significant associations were found for the 'Grains', 'Processed Foods' and 'Legumes, Fruits & Vegetables' protein factors. Additional adjustments for the participants' vegetarian dietary pattern and nutrients related to cardiovascular disease outcomes did not change the results. Conclusions: Associations between the 'Meat' and 'Nuts & Seeds' protein factors and cardiovascular outcomes were strong and could not be ascribed to other associated nutrients considered to be important for cardiovascular health. Healthy diets can be advocated based on protein sources, preferring low contributions of protein from meat and higher intakes of plant protein from nuts and seeds.

Towards using a full spectrum of early clinical trial data: a retrospective analysis to compare potential longitudinal categorical models for molecular targeted therapies in oncology.

Following the pattern of phase I clinical trials for cytotoxic drugs, dose-finding clinical trials in oncology of molecularly targeted agents (MTA) aim at determining the maximum tolerated dose (MTD). In classical phase I clinical trials, MTD is generally defined by the number of patients with short-term major treatment toxicities (usually called dose-limiting toxicities, DLT), occurring during the first cycle of study treatment (e.g. within the first 3weeks of treatment). However, S. Postel-Vinay (2011) highlighted that half of grade 3 to 4 toxicities, usually considered as DLT, occur after the first cycle of MTA treatment. In addition, MTAs could induce other moderate (e.g. grade 2) toxicities which could be taken into account depending on their clinical importance, chronic nature and duration. Ignoring these late toxicities may lead to an underestimation of the drug toxicity and to wrong dose recommendations for phase II and III clinical trials. Some methods have been proposed, such as the time-to-event continuous reassessment method (Cheung 2000 and Mauguen 2011), to take into account the late toxicities. We suggest approaches based on longitudinal models (Doussau 2013). We compare several models for longitudinal data, such as transitional or marginal models, to take into account all relevant toxicities occurring during the entire length of the patient treatment (and not just the events within a predefined short-term time-window). These models allow the statistician to benefit from a larger amount of safety data which could potentially improve that accuracy in MTD assessment.

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. Anionic phospholipids, phosphatidylserine (PS) and phosphoinositides (PPI) provide an important attachment site for numerous proteins, including several oxidase subunits. RESULTS: We investigated the kinetics of p47(phox) and Rac2 phagosomal membrane recruitment. Both subunits are known to interact with anionic phospholipids; we therefore addressed the role of PS in this recruitment. Phagosomal accumulation of p47(phox) and Rac2 tagged with fluorescent proteins was analysed by videomicroscopy. We used the C2 domain of lactadherin (lactC2) that interacts strongly and specifically with PS to monitor intracellular PS localisation and to decrease PS accessibility. During phagocytosis of opsonised zymosan, p47(phox) and constitutively active Rac2G12V briefly translocated to the phagosomal membrane, whereas ROS production continued for a longer period. However, in the presence of lactC2, Rac2G12V recruitment was inhibited and the kinetics of p47(phox) recruitment and detachment were delayed. A reduced phagosomal ROS production was also observed during the first 7 min following the phagosome closure. CONCLUSIONS: These results suggest that p47(phox) and Rac2 accumulate only transiently at the phagosome at the onset of NADPH activity and detach from the phagosome before the end of ROS production. Furthermore, lactC2, by masking PS, interfered with the phagosomal recruitment of p47(phox) and Rac2 and disturbed NADPH oxidase activity. Thus, PS appears as a modulator of NADPH oxidase activation.

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. Individual neighbouring states are dependent, like in reality and they exhibit their own dynamics with patients transitioning between low and high disease states, according to a set of transition probabilities. Our methodology enables estimation of unobserved disease dynamics and daily seizure frequencies in all disease states. Additional modes of drug action are achievable: gabapentin may influence both daily seizure frequencies and disease state dynamics. Gabapentin significantly reduced seizure frequencies in both disease activity states; however it did not significatively affect disease dynamics. Mixed hidden Markov modeling is able to mimic dynamics of seizure frequencies very well. It offers novel insights into understanding disease dynamics in epilepsy and gabapentin mode of action.