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Background: Children's growth is increasingly considered a key mediator of later life outcomes. When examining weight growth, the correlation between repeated observations on the same subject must be regarded as well-modelled. This study aimed to analyze children's weight growth variations and associated factors in Ethiopia, India, Peru, and Vietnam using a fractional polynomial mixed-effects model. Methods: This study used longitudinal data from the Young Lives Cohort Study conducted from 2002 to 2016 in Ethiopia, India, Peru, and Vietnam. The study included 7,140 children of 1 to 15 years old A fractional polynomial mixed-effects model was used to analyze the data. Results: Ethiopian, Peruvian, and Vietnamese children had significantly higher average body weights than children in India (1.426, P<0.001; 1.992, P<0.001; 1.334, P<0.001, respectively). Girl children's average body weight was significantly 0.15 times less than that of boys (-0.148; P=0.027). The average weight of rural children was significantly 0.671 times less than that of urban children (0.671, P<0.001). Children from Peru and Vietnam had higher rates of weight change than those from India. However, the rate of weight change was lower in Ethiopian children than in Indian children. Children from urban areas had a significantly higher rate of weight gain than those from rural areas. Conclusion: Country, sex, residence, parental education, household size, wealth, good drinking water, and reliable power affected children's longitudinal weight growth. Therefore, WHO and the nation's health ministry should monitor children's weight growth status and these associated factors to plan future action.

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Human immunodeficiency virus (HIV) dynamics have been the focus of epidemiological and biostatistical research during the past decades to understand the progression of acquired immunodeficiency syndrome (AIDS) in the population. Although there are several approaches for modeling HIV dynamics, one of the most popular is based on Gaussian mixed-effects models because of its simplicity from the implementation and interpretation viewpoints. However, in some situations, Gaussian mixed-effects models cannot (a) capture serial correlation existing in longitudinal data, (b) deal with missing observations properly, and (c) accommodate skewness and heavy tails frequently presented in patients' profiles. For those cases, mixed-effects state-space models (MESSM) become a powerful tool for modeling correlated observations, including HIV dynamics, because of their flexibility in modeling the unobserved states and the observations in a simple way. Consequently, our proposal considers an MESSM where the observations' error distribution is a skew-t. This new approach is more flexible and can accommodate data sets exhibiting skewness and heavy tails. Under the Bayesian paradigm, an efficient Markov chain Monte Carlo algorithm is implemented. To evaluate the properties of the proposed models, we carried out some exciting simulation studies, including missing data in the generated data sets. Finally, we illustrate our approach with an application in the AIDS Clinical Trial Group Study 315 (ACTG-315) clinical trial data set.

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OBJECTIVE: To study the trend of colorectal cancer (CRC) incidence, considering its spatiotemporal distribution and its association with the level of urbanisation in Córdoba, Argentina (2004-2014). METHODS: An ecological and longitudinal study was conducted in the province of Córdoba (the second most populated of the country) using annual data for the period 2004-2014. Age-standardised incidence rates (ASIR) (standard national and world population) by sex were calculated for Córdoba and its 26 departments for CRC using the provincial tumour registry database. Joinpoint regression models were adjusted to provincial ASIRs. Departments ASIRs were mapped in quintiles. The departments were grouped into three strata according to urbanisation: High (n1=6, >107,000 people); Intermediate (n2=13, 33,000-107,000 people); and Low (n3=7, <33,000 people). The spatio-temporal correlation analysis of the departments' rates was fulfilled using the multilevel modelling strategy. RESULTS: ASIRs of CRC in Córdoba province were 30.9 ±1.5 and 24.3 ±1.5 cases/100,000 for men and women, respectively. During 2004-2014, ASIRs tended to decrease (annual per cent change: -0.6; CI95% -1.8, 0.6). Maps reflected different geospatial patterns by sex. CRC incidence in males was higher than in females in all strata (high urbanisation, incidence rate ratios (IRR): 1.66; intermediate, IRR: 1.59; and low, IRR: 1.40). There was a significant downward temporary variation in the most populous departments (3% per year). CONCLUSIONS: CRC presents a non-random spatial pattern across the territory with a decreasing temporal variation in the most populous departments. Differential incidence and temporospatial tendency burden in Córdoba involve sex and urbanisation. Men continue to be the population at the greatest risk; this pattern is more noticeable in most urban settings.

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The interest for nonlinear mixed-effects models comes from application areas as pharmacokinetics, growth curves and HIV viral dynamics. However, the modeling procedure usually leads to many difficulties, as the inclusion of random effects, the estimation process and the model sensitivity to atypical or nonnormal data. The scale mixture of normal distributions include heavy-tailed models, as the Student-t, slash and contaminated normal distributions, and provide competitive alternatives to the usual models, enabling the obtention of robust estimates against outlying observations. Our proposal is to compare two estimation methods in nonlinear mixed-effects models where the random components follow a multivariate scale mixture of normal distributions. For this purpose, a Monte Carlo expectation-maximization algorithm (MCEM) and an efficient likelihood-based approximate method are developed. Results show that the approximate method is much faster and enables a fairly efficient likelihood maximization, although a slightly larger bias may be produced, especially for the fixed-effects parameters. A discussion on the robustness aspects of the proposed models are also provided. Two real nonlinear applications are discussed and a brief simulation study is presented.

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Many health care professionals and institutions manage longitudinal databases, involving follow-ups for different patients over time. Longitudinal data frequently manifest additional complexities such as high variability, correlated measurements and missing data. Mixed effects models have been widely used to overcome these difficulties. This work proposes the use of linear mixed effects models as a tool that allows to search conceptually different types of anomalies in the data simultaneously.

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OBJECTIVE: The objective of this study was to compare the quality of life (QoL) for parents of children with inborn errors of metabolism (IEMs) requiring a restricted diet with French population norms and investigate parental QoL determinants. STUDY DESIGN: This cross-sectional study included mothers and/or fathers of children < 18 years of age affected by IEMs requiring a restricted diet (except phenylketonuria) from January 2015 to December 2017. Parents' QoL was assessed using the World Health Organization Quality of Life BREF questionnaire and compared with age- and sex-matched reference values from the French general population. Linear mixed models were used to examine the effects of demographic, socioeconomic, disease-related, and psychocognitive factors on parental QoL, according to a 2-level regression model considering individuals (parents) nested within families. RESULTS: Of the 1156 parents invited to participate, 785 (68%) were included. Compared with the general population, parents of children with IEMs requiring a restricted diet reported a lower QoL in physical and social relationship domains but a higher QoL in the psychological domain. In the multivariate analysis, characteristics associated with poorer parental QoL included both parent-related factors (being a father, older age, more educated parent, nonworking parent, greater anxiety, seeking more social support, and using less positive thinking and problem-solving coping strategies) and family-related factors (disease complications, increased number of hospital medical providers, child's younger age, single-parent family, and lower family material wealth). CONCLUSION: Parents of children with IEMs requiring a restricted diet reported poorer QoL in physical and social relationship domains than population norms. Psychocognitive factors, beyond disease-specific and family-related characteristics, were the most important determinants influencing parental QoL and may represent essential aspects for interventions. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov: NCT02552784.

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This article extends the semiparametric mixed model for longitudinal censored data with Gaussian errors by considering the Student's t $$ t $$ -distribution. This model allows us to consider a flexible, functional dependence of an outcome variable over the covariates using nonparametric regression. Moreover, the proposed model takes into account the correlation between observations by using random effects. Penalized likelihood equations are applied to derive the maximum likelihood estimates that appear to be robust against outlying observations with respect to the Mahalanobis distance. We estimate nonparametric functions using smoothing splines under an EM-type algorithm framework. Finally, the proposed approach's performance is evaluated through extensive simulation studies and an application to two datasets from acquired immunodeficiency syndrome clinical trials.

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The accurate prediction of energy requirements for healthy individuals has many useful applications. The occupational perspective has also been proven to be of great utility for improving workers' ergonomics, safety, and health. This work proposes a statistical regression model based on actigraphy and personal characteristics to estimate energy expenditure and cross-validate the results with reference standardized methods. The model was developed by hierarchical mixed-effects regression modeling based on the multitask protocol data. Measurements combined actigraphy, indirect calorimetry, and other personal and lifestyle information from healthy individuals (n = 50) within the age of 29.8 ± 5 years old. Results showed a significant influence of the variables related to movements, heart rate and anthropometric variables of body composition for energy expenditure estimation. Overall, the proposed model showed good agreement with energy expenditure measured by indirect calorimetry and evidenced a better performance than the methods presented in the international guidelines for metabolic rate assessment proving to be a reliable alternative to normative guidelines. Furthermore, a statistically significant relationship was found between daily activity and energy expenditure, which raised the possibility of further studies including other variables, namely those related to the subject's lifestyle.

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In longitudinal studies involving laboratory-based outcomes, repeated measurements can be censored due to assay detection limits. Linear mixed-effects (LMEs) models are a powerful tool to model the relationship between a response variable and covariates in longitudinal studies. However, the linear parametric form of linear mixed-effect models is often too restrictive to characterize the complex relationship between a response variable and covariates. More general and robust modeling tools, such as nonparametric and semiparametric regression models, have become increasingly popular in the last decade. In this article, we use semiparametric mixed models to analyze censored longitudinal data with irregularly observed repeated measures. The proposed model extends the censored linear mixed-effect model and provides more flexible modeling schemes by allowing the time effect to vary nonparametrically over time. We develop an Expectation-Maximization (EM) algorithm for maximum penalized likelihood estimation of model parameters and the nonparametric component. Further, as a byproduct of the EM algorithm, the smoothing parameter is estimated using a modified linear mixed-effects model, which is faster than alternative methods such as the restricted maximum likelihood approach. Finally, the performance of the proposed approaches is evaluated through extensive simulation studies as well as applications to data sets from acquired immune deficiency syndrome studies.

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Unique combinations of geographic and environmental conditions make quantifying the importance of factors that influence forest productivity difficult. I aimed to model the height growth of dominant Nothofagus alpina trees in temperate forests of Chile, as a proxy for forest productivity, by building a dynamic model that accounts for topography, habitat type, and climate conditions. Using stem analysis data of 169 dominant trees sampled throughout south-central Chile (35°50' and 41°30' S), I estimated growth model parameters using a nonlinear mixed-effects framework that takes into account the hierarchical structure of the data. Based on the proposed model, I used a system-dynamics approach to analyze growth rates as a function of topographic, habitat type, and climatic variability. I found that the interaction between aspect, slope, and elevation, as well as the effect of habitat type, play an essential role in determining tree height growth rates of N. alpina. Furthermore, the precipitation in the warmest quarter, precipitation seasonality, and annual mean temperature are critical climatic drivers of forest productivity. Given a forecasted climate condition for the region by 2100, where precipitation seasonality and mean annual temperature increase by 10% and 1°C, respectively, and precipitation in the warmest quarter decreases by 10 mm, I predict a reduction of 1.4 m in height growth of 100-yr-old dominant trees. This study shows that the sensitivity of N. alpina-dominated forests to precipitation and temperature patterns could lead to a reduction of tree height growth rates as a result of climate change, suggesting a decrease in carbon sequestration too. By implementing a system dynamics approach, I provide a new perspective on climate-productivity relationships, bettering the quantitative understanding of forest ecosystem dynamics under climate change. The results highlight that while temperature rising might favor forest growth, the decreasing in both amount and distribution within a year of precipitation can be even more critical to reduce forest productivity.

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Count data sets are traditionally analyzed using the ordinary Poisson distribution. However, such a model has its applicability limited as it can be somewhat restrictive to handle specific data structures. In this case, it arises the need for obtaining alternative models that accommodate, for example, (a) zero-modification (inflation or deflation at the frequency of zeros), (b) overdispersion, and (c) individual heterogeneity arising from clustering or repeated (correlated) measurements made on the same subject. Cases (a)-(b) and (b)-(c) are often treated together in the statistical literature with several practical applications, but models supporting all at once are less common. Hence, this paper's primary goal was to jointly address these issues by deriving a mixed-effects regression model based on the hurdle version of the Poisson-Lindley distribution. In this framework, the zero-modification is incorporated by assuming that a binary probability model determines which outcomes are zero-valued, and a zero-truncated process is responsible for generating positive observations. Approximate posterior inferences for the model parameters were obtained from a fully Bayesian approach based on the Adaptive Metropolis algorithm. Intensive Monte Carlo simulation studies were performed to assess the empirical properties of the Bayesian estimators. The proposed model was considered for the analysis of a real data set, and its competitiveness regarding some well-established mixed-effects models for count data was evaluated. A sensitivity analysis to detect observations that may impact parameter estimates was performed based on standard divergence measures. The Bayesian p -value and the randomized quantile residuals were considered for model diagnostics.

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Cefquinome is a fourth-generation cephalosporin that is used empirically in goats. Different physiologic factors like pregnancy or lactation could determine the pharmacokinetic behavior of drugs in the organism. The objectives of this study are to (a) compare the pharmacokinetics of cefquinome after intravenous and intramuscular administration in adult nonpregnant (n = 6), pregnant (n = 6), and lactating goats (n = 6), at a dose of 2 mg/kg, with rich sampling by nonlinear mixed-effects modeling, (b) conduct a pharmacokinetic/pharmacodynamic analysis to evaluate the efficacy of the recommended posology in goats with different physiological states, and (c) determine the optimal posology that achieve a PTA value ≥ 90%, taking into account a T > MIC ≥ 60% of a MIC value ≤ 0.25 µg/ml, in the different subpopulations of goats for both routes. Gestation significantly increased Ka and V1, while reduced F0, Cl, and Q. On the other hand, lactation significantly increased V1 and reduced Tk0. Cefquinome concentrations achieved in placental cotyledon, amniotic fluid, and fetal serum indicate a minimal penetration across the placental barrier. Moreover, milk penetration of cefquinome was minimal. The total body clearance of cefquinome for goats was 0.29 L kg-1  hr-1 , that is apparently higher than the reported for cows (0.13 L kg-1  hr-1 ) and pigs (0.16 L kg-1  hr-1 ). So, the optimal dose regimen for cefquinome after intravenous and intramuscular administration required higher dose and frequency of administration compared with recommendations for cows or pigs. Therefore, 2 mg kg-1  8 hr-1 and 5 mg kg-1  12 hr-1 could be used for IV and IM routes, respectively, for the treatment of respiratory infections caused by P. multocida and M. haemolytica, but only 5 mg kg-1  12 hr-1 by both routes should be recommended for Escherichia coli infections.

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Mixed-effects models, with modifications to accommodate censored observations (LMEC/NLMEC), are routinely used to analyze measurements, collected irregularly over time, which are often subject to some upper and lower detection limits. This paper presents a likelihood-based approach for fitting LMEC/NLMEC models with autoregressive of order p dependence of the error term. An EM-type algorithm is developed for computing the maximum likelihood estimates, obtaining as a byproduct the standard errors of the fixed effects and the likelihood value. Moreover, the constraints on the parameter space that arise from the stationarity conditions for the autoregressive parameters in the EM algorithm are handled by a reparameterization scheme, as discussed in Lin and Lee (2007). To examine the performance of the proposed method, we present some simulation studies and analyze a real AIDS case study. The proposed algorithm and methods are implemented in the new R package ARpLMEC.

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Suboptimal choice is the preference for a discriminative alternative with low probability of reinforcement, over a non-discriminative alternative with higher probability of reinforcement. Pigeons consistently prefer the discriminative alternative, whereas rats prefer the non-discriminative; the variables accounting for this difference are not yet clear. The economic concepts related to demand curves have been used to calculate the essential value of different types of reinforcers, so they may be useful to compare the value of the alternatives in the suboptimal choice procedure. The goal of this study was to calculate the essential value of each of the alternatives presented in the suboptimal choice procedure to assess if pigeons (Experiment 1) and rats (Experiment 2) value them differently. In both experiments, the fixed ratio requirement in the initial link was increased throughout sessions in order to obtain the demand curve and calculate the essential value by fitting the exponential-demand model. A Bayesian Linear Mixed-Effects Model indicated that pigeons had higher essential values for the discriminative alternative, whereas rats obtained higher essential values for the non-discriminative alternative. These results suggest that preferences in the suboptimal choice procedure are indeed based on the essential value of the alternatives, and provide a new paradigm to study the variables affecting this phenomenon.

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This article demonstrates the power and flexibility of linear mixed-effects models (LMEMs) to investigate high-density surface electromyography (HD-sEMG) signals. The potentiality of the model is illustrated by investigating the root mean squared value of HD-sEMG signals in the tibialis anterior muscle of healthy (n = 11) and individuals with diabetic peripheral neuropathy (n = 12). We started by presenting the limitations of traditional approaches by building a linear model with only fixed effects. Then, we showed how the model adequacy could be increased by including random effects, as well as by adding alternative correlation structures. The models were compared with the Akaike information criterion and the Bayesian information criterion, as well as the likelihood ratio test. The results showed that the inclusion of the random effects of intercept and slope, along with an autoregressive moving average correlation structure, is the one that best describes the data (p < 0.01). Furthermore, we demonstrate how the inclusion of additional variance structures can accommodate heterogeneity in the residual analysis and therefore increase model adequacy (p < 0.01). Thus, in conclusion, we suggest that adopting LMEM to repeated measures such as electromyography can provide additional information from the data (e.g. test for alternative correlation structures of the RMS value), and hence provide new insights into HD-sEMG-related work.

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The maximum entropy theory of ecology (METE) applies the concept of "entropy" from information theory to predict macroecological patterns. The energetic predictions of the METE rely on predetermined metabolic scaling from external theories, and this reliance diminishes the testability of the theory. In this work, I build parameterized METE models by treating the metabolic scaling exponent as a free parameter, and I use the maximum-likelihood method to obtain empirically plausible estimates of the exponent. I test the models using the individual tree data from an oak-dominated deciduous forest in the northeastern United States and from a tropical forest in central Panama. My analysis shows that the metabolic scaling exponents predicted from the parameterized METE models deviate from that of the metabolic theory of ecology and exhibit large variation, at both community and population levels. Assemblage and population abundance may act as ecological constraints that regulate the individual-level metabolic scaling behavior. This study provides a novel example of the use of the parameterized METE models to reveal the biological processes of individual organisms. The implication and possible extensions of the parameterized METE models are discussed.

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Dengue fever is a viral disease transmitted by the mosquito Aedes aegypti. In order to avoid epidemics and deaths, this transmitting vector must be controlled. This work assembles, for the first time, data from multiple governmental bodies describing the number of dengue cases reported, and meteorological conditions in 20 cities in the Goiás state, Brazil, from 2008 to 2015. We then apply generalised linear mixed modelling to this novel data set to model dengue occurrences in this state, where the tropical climate favours the proliferation of the main transmitting vector of this disease. The number of reported dengue cases is estimated using meteorological variables as fixed effects, and city and year data are included in the model as random effects. The proposed models can cope with complex data structures, such as nested data, while taking into account the particularities of each year dependent on the city under analysis. The results confirm that precipitation, minimum temperature, and relative air humidity contribute to the increase of dengue cases number, while year and city location are determining factors. Public policies, based on these new results, together with joint actions involving local populations, are essential to combat the vector transmitting dengue and avoid epidemics.

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OBJECTIVE: To analyze longitudinal trends of pulmonary function testing in patients with congenital diaphragmatic hernia (CDH) followed in our multidisciplinary clinic. STUDY DESIGN: This was a retrospective cohort study of CDH patients born between 1991 and 2013. A linear mixed effects model was fitted to estimate the trends of percent predicted forced expiratory volume in 1 second (FEV1pp), percent predicted forced vital capacity (FVCpp), and FEV1/FVC over time. RESULTS: Of 268 patients with CDH who survived to discharge, 119 had at least 1 pulmonary function test study. The FEV1pp (P < .001), FVCpp (P = .017), and FEV1/FVC (P = .001) decreased with age. Compared with defect size A/B, those with defect size C/D had lower FEV1pp by an average of 11.5% (95% CI, 2.9%-20.1%; P = .010). A history of oxygen use at initial hospital discharge also correlated with decreased FEV1pp by an average of 8.0% (95% CI, 1.2%-15.0%; P = .023). CONCLUSIONS: In a select cohort of CDH survivors, average pulmonary function declines with age relative to expected population normative values. Those with severe CDH represent a population at risk for worsening pulmonary function test measurements who may benefit from recognition and monitoring for complications.

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Bromopride is a prokinetic and antiemetic drug used to treat nausea and vomiting. Although its prescription is common in Brazil, there is a lack of studies about bromopride pharmacokinetics. Therefore, the aims of this study were to investigate the population pharmacokinetics of bromopride and to evaluate the influence of covariates on its absorption. This study is a retrospective analysis of data collected from bioequivalence studies. The data was modeled using MONOLIX 2018R2. Assuming one-compartment and linear elimination, the absorption phase was evaluated with different structural models. The model of sequential first- and zero-order with combined error and exponential inter-individual variability in all parameters best described the atypical absorption profile of bromopride. Population estimates were first-order absorption rate (ka) of 0.08 h - 1, fraction of dose absorbed by first-order (Fr) of 32.60%, duration of the zero-order absorption (Tk0) of 0.88 h with latency time (Tlag) of 0.47 h, volume of distribution of 230 l and clearance of 46.80 l h - 1. Bodyweight affects Tk0, dosage form was found to correlate with Tk0 and Tlag, while gender affects Tlag. However, simulations evaluating the clinical importance of these covariates on steady-state indicated minimal changes on bromopride exposure. The mixed absorption model was reasonable to describe the absorption process of bromopride because it had the flexibility to fit multiple-peaks profile and shows good agreement with physicochemical properties of drug.

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Lactation records from cows of the southwestern Paraná state, Brazil, form the dataset of this study. We applied the information-theoretic approach to evaluate the ability of the nonlinear Wood, Brody, Dijkstra, and Gamma functions to fit to these data by employing a two-step technique based on nonlinear mixed-effects models and generalized linear mixed-effects models. Wood's equation was fitted with the combination of a first-order autoregressive correlation structure and a variance function to account for heteroscedasticity. This version was the best choice to mimic lactation records. Some geometric attributes of Wood's model were deduced, mainly the ascending specific rate from parturition to peak milk yield and the descending specific rate as a measure of the lactation persistence of the milk yield at peak production. Breed and parity order of the cows were assumed as fixed effects to obtain a reliable model fitting process. Regardless of breed, first-order parity cows had greater persistency than their older counterparts, and the greater the ascending rate of milk yield from the parturition to the peak, the sharper the decrease in milk yield post-peak; therefore, the rates (absolute values) of ascending and descending phases correlated positively. Nonetheless, the actual estimated values of the descending phase rates are negative. Wood's equation was flexible enough to mimic either concave- and convex-shaped lactation profiles. The correlations between both peak milk yield and random estimates for ß with total milk yield per lactation were positive. However, peak milk yield might not be the only variable used for ranking cows; the total milk yield integrates all information of the lactation profile through the estimated parameters of Wood's equation.(AU)

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