Search

This paper considers a widely used mixed effects model in repeated measures under heteroscedasticity. Hypotheses of the equality of the fixed effects and the simultaneous confidence intervals for all pair-wise differences are discussed. A generalized F-test has been proposed to test the equality of the fixed effects in the model, but simulation results for evaluating its performance have not been shown in the literature. Moreover, the generalized F-test cannot be used to deduce the simultaneous confidence intervals for all pair-wise differences of the fixed effects. The authors propose two new p-values to test the hypotheses of equality of the fixed effects and simultaneous confidence intervals of the differences of the effects based on the generalized pivotal quantities derived in this paper. The authors also compare the empirical performances of the proposed tests and the generalized F-test. The type I error rates and powers of these tests are evaluated using the Monte Carlo simulation. The simulation studies show that the generalized F-test does not perform well in terms of type I error rate under various sample size and parameter combinations. However, the type I error probabilities of the proposed tests are always close to the nominal value. It can also be seen that the simultaneous confidence intervals perform well. [ABSTRACT FROM AUTHOR]

In this case study, we investigate the degradation process of light-emitting diodes (LEDs), which is used as a light source in DNA sequencing machines. Accelerated degradation tests are applied by varying temperature and forward current, and the light outputs are measured by a computerized measuring system. A degradation path model, which connects to the LED function recommended in Mitsuo (1991), is used in describing the degradation process. We consider variations in both measurement errors and degradation paths among individual test units. It is demonstrated that the hierarchical modeling approach is flexible and powerful in modeling a complex degradation process with nonlinear function and random coefficient. After fitting the model by maximum likelihood estimation, the failure time distribution can be obtained by simulation. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Reaction behaviors and kinetics of catalytic oxidation of benzene, toluene, and ethyl acetate with feed concentrations in the range of 700–5,000 ppm over Pd/ZSM-5 catalyst were investigated. Results for single components show that ethyl acetate ( T50 = 190–200°C) is more easily oxidized than benzene ( T50 = 215–225°C) and toluene ( T50 = 225–235°C). The conversion of ethyl acetate was increased with the increase of its feeding concentration, while the opposite behaviors were observed for benzene and toluene as their conversion rates were decreased with the increase of the inlet concentration. Different behaviors were observed in catalytic oxidation of volatile organic compound (VOC) multi-components, the presence of benzene or toluene inhibits the conversion of ethyl acetate, and the aromatic hydrocarbons inhibit each other in all cases. Ethyl acetate possesses obvious inhibitory effect on benzene oxidation, while it is interesting to note that ethyl acetate has a promotion effect on toluene conversion. The kinetic data were fitted by the Power-law and Mars–van Krevelen kinetic models. The fitting result shows that the Power-law model is more suitable for predicting the conversion of benzene than the other VOCs, and the Mars–van Krevelen model can accurately express the reaction rate of all investigated VOCs. [ABSTRACT FROM AUTHOR]

Abstract: This work aims to predict exponentials of mixed effects under a multivariate linear regression model with one random factor. Such quantities are of particular interest in prediction problems where the dependent variable is the logarithm of the variable that is the object of inference. Bias-corrected empirical predictors of the target quantities are defined. A second-order approximation for the mean crossed product error of two of these predictors is obtained, where the mean squared error is a particular case. An estimator of the mean crossed product error with second-order bias is proposed. Finally, results are illustrated through an application related to small area estimation. [Copyright &y& Elsevier]

Background: Time-series and case-crossover are two techniques that are widely used for assessing the short-term impact of ambient air pollution exposure on health.Materials and Methods: The generalized linear mixed model (GLMM) methodology is proposed here to study the association between ambient air pollution and health outcomes. Poisson random-effects models are applied to analyze the clustered counts, where the groups of days, determined by the triplet , form the clusters. The proposed technique uses a nested structure for the clusters and allows random-effects for hierarchical factors. A random intercept in the models adjusts for different levels of counts among the clusters. A fixed slope represents a common response to the exposure.Results and Conclusions: The obtained results are consistent with those generated by a classical approach (for example the case-crossover technique). The GLMM technique is a valid alternative methodology for studying air health effects. [ABSTRACT FROM AUTHOR]

Abstract: Clinical HIV-1 data include many individual factors, such as compliance to treatment, pharmacokinetics, variability in respect to viral dynamics, race, sex, income, etc., which might directly influence or be associated with clinical outcome. These factors need to be taken into account to achieve a better understanding of clinical outcome and mathematical models can provide a unifying framework to do so. The first objective of this paper is to demonstrate the development of comprehensive HIV-1 dynamics models that describe viral dynamics and also incorporate different factors influencing such dynamics. The second objective of this paper is to describe alternative estimation methods that can be applied to the analysis of data with such models. In particular, we consider: (i) simple but effective two-stage estimation methods, in which data from each patient are analyzed separately and summary statistics derived from the results, (ii) more complex nonlinear mixed effect models, used to pool all the patient data in a single analysis. Bayesian estimation methods are also considered, in particular: (iii) maximum posterior approximations, MAP, and (iv) Markov chain Monte Carlo, MCMC. Bayesian methods incorporate prior knowledge into the models, thus avoiding some of the model simplifications introduced when the data are analyzed using two-stage methods, or a nonlinear mixed effect framework. We demonstrate the development of the models and the different estimation methods using real AIDS clinical trial data involving patients receiving multiple drugs regimens. [Copyright &y& Elsevier]

The celebrated simplicity of the EM algorithm is somewhat lost in its common use for generalized linear mixed models (GLMMs) because of its analytically intractable E-step. A natural and typical strategy in practice is to implement the E-step via Monte Carlo by drawing the unobserved random effects from their conditional distribution as specified by the E-step. In this paper, we show that further augmenting the missing data (e.g., the random effects) used by the M-step leads to a quite attractive and general slice sampler for implementing the Monte Carlo E-step. The slice sampler scheme is straightforward to implement, and it is neither restricted to the particular choice of the link function (e.g., probit) nor to the distribution of the random effects (e.g., normal). We apply this scheme to the standard EM algorithm as well as to an alternative EM algorithm which treats the variance-standardized random effects, rather than the random effects themselves, as missing data. The alternative EM algorithm does not only have faster convergence, but also leads to generalized linear model-like variance estimation, because it converts the random-effect standard deviations into linear regression parameters. Using the well-known salamander mating problem, we compare these two algorithms with each other, as well as with a variety of methods given in the literature in terms of the resulting point and interval estimates. [ABSTRACT FROM AUTHOR]

Positron-Emission Tomography (PET) is an imaging technology currently used in drug development as a non-invasive measure of drug distribution and interaction with biochemical target system. The level of receptor occupancy achieved by a compound can be estimated by comparing time-activity measurements in an experiment done using tracer alone with the activity measured when the tracer is given following administration of unlabelled compound. The effective use of this surrogate marker as an enabling tool for drug development requires the definition of a model linking the brain receptor occupancy with the fluctuation of plasma concentrations. However, the predictive performance of such a model is strongly related to the precision on the estimate of receptor occupancy evaluated in PET scans collected at different times following drug treatment. Several methods have been proposed for the analysis and the quantification of the ligand-receptor interactions investigated from PET data. The aim of the present study is to evaluate alternative parameter estimation strategies based on the use of non-linear mixed effect models allowing to account for intra and inter-subject variability on the time-activity and for covariates potentially explaining this variability. A comparison of the different modeling approaches is presented using real data. The results of this comparison indicates that the mixed effect approach with a primary model partitioning the variance in term of Inter-Individual Variability (IIV) and Inter-Occasion Variability (IOV) and a second stage model relating the changes on binding potential to the dose of unlabelled drug is definitely the preferred approach. [Copyright &y& Elsevier]

The impacts of natural disasters on inbound tourism have recently received growing attention due to the increased frequency of natural disasters. Previous literature has reached different conclusions on the effects of disaster severity and frequency, and few studies have considered both the severity and frequency of natural disasters in the same statistical model. The synergistic effect of these two attributes of natural disasters is unrevealed herein. To enhance the current knowledge, this paper assesses the impacts of both the severity and frequency of natural disasters on the inbound tourist flows of destination countries from an annual perspective. We integrate 258,401 bilateral tourist movements and 9892 natural disasters in 177 countries between 1995 and 2018 and analyze the data using a panel regression. The results show that disaster severity deters tourists from visiting destinations, but disaster frequency indicates a positive effect. We also identify the positive moderating effect of disaster frequency. • The impacts of multiple natural disasters are researched from an annual perspective. • Disaster frequency is identified as a vital variable to assess the impact of natural disaster from an annual perspective. • The synergistic effect of the frequency and severity of natural disaster on inbound tourist flows is empirically examined. • Natural disaster severity negatively influences inbound tourist flows, but disaster frequency indicates a positive effect. • Natural disaster frequency positively moderates the relationship between natural disaster severity and inbound tourist flows. [ABSTRACT FROM AUTHOR]

A rear-end crash is a widely studied type of road accident. The road area at the crash scene is a factor that significantly affects the crash severity from rear-end collisions. These road areas may be classified as urban or rural and evince obvious differences such as speed limits, number of intersections, vehicle types, etc. However, no study comparing rear-end crashes occurring in urban and rural areas has yet been conducted. Therefore, the present investigation focused on the comparison of diverse factors affecting the likelihood of rear-end crash severities in the two types of roadways. Additionally, hierarchical logistic models grounded in a spatial basis concept were applied by determining varying parameter estimations with regard to road segments. Additionally, the study compared coefficients with multilevel correlation model and those without multilevel correlation. Four models were established as a result. The data used for the study pertained to rear-end crashes occurring on Thai highways between 2011 and 2015. The results of the data analysis revealed that the model parameters for both urban and rural areas are in the same direction with the larger number of significant parameter values present in the rural rear-end crash model. The significant variables in both the urban and rural road segment models are the seat belt use, and the time of the incident. To conclude, the present study is useful because it provides another perspective of rear-end crashes to encourage policy makers to apply decisions that favor rules that assure safety., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

In recent years, there has been growing interest among researchers in exploring approximate number sense (ANS)—the ability to estimate and discriminate quantities without the use of symbols. Despite the growing number of studies on ANS, there have been no cross‐cultural longitudinal studies to estimate both the development of ANS and the cross‐cultural differences in ANS growth trajectories. In this study, we aimed to estimate the developmental trajectories of ANS from the beginning of formal education to the end of elementary school in two countries, Russia and Kyrgyzstan, which have similar organization of their educational systems but differences in socioeconomic status (SES) and in the results of large‐scale educational assessments. To assess the developmental trajectories of ANS, we used a four‐wave longitudinal study with 416 participants from two countries and applied the mixed effect growth approach and the latent class growth approach. Our analysis revealed that the rate of growth in ANS accuracy was higher for the Russian sample than for the Kyrgyz sample and that this difference remained significant even after controlling for fluid intelligence. We identified two latent classes of growth trajectories: the first class had a significant growth in ANS, whereas the second class had no growth. Comparing the distribution of latent classes within the two countries revealed that there was a significantly larger proportion of schoolchildren from the second class in Kyrgyzstan than in Russia. [ABSTRACT FROM AUTHOR]

Based on the investigation data of seedlings and saplings from 48 plots in natural broad-leaved forest of Maoershan Experimental Forest Farm of Northeast Forestry University in Heilongjiang Province, the optimum model of ground diameter (D 0 ) - height (H) was selected from eight alternative models as the basic model for the main regeneration tree species, and then the stand factors were parameterized, and the mixed effect model of sampling plot level was developed. The basic model and the mixed effect model were tested by independent samples. The results showed that there was a significant positive correlation between ground diameter and tree height of seedlings and saplings and that power function or model containing power function could better fit the relationship. The introduction of stand factors [dominant height of forest (H T ), average diameter at breast height (D g ), basal area of forest (BA)] could improve the fitting effect of the model, with the residual root mean square error (RMSE) of each tree species decreasing by 1.3%-7.4% (average 3.8%), adjusted coefficient of determination (R a ) only increasing by 0.1%-1.1% (average 0.6%) and Akaike info criterion (AIC) decreasing by 3.2%-35.2% (average 11.4%). Mixed effect models were developed for 10 tree species, such as Ulmus propinqua, Tilia and Fraxinus mandshurica. The R 2 of mixed effect models was larger than that of the basic model, with an enhancement of 0.5%-3.5% (average 2.2%). RMSE and AIC decreased by 3.9%-20.3% (ave-rage 13.9%) and 4.0%-44.4% (average 22.3%) than that of the basic model. Model test results showed that, compared with the basic model, the average absolute error (MAE) of mixed effect model was reduced by 0.0001-0.46 m, with an average reduction of 0.08 m, and the average prediction error percentage (MPSE) decreased by 0.1%-6.2%, with an average reduction of 2.0%. The mixed effect model could improve the fitting effect and prediction ability of the model. The ground diameter-height model of seedlings and samplings of main regeneration species in broad-leaved mixed forest was developed in this study, which provides a reference for structure analysis and stand growth prediction of natural broad-leaved forest.a 2 of mixed effect models was larger than that of the basic model, with an enhancement of 0.5%-3.5% (average 2.2%). RMSE and AIC decreased by 3.9%-20.3% (ave-rage 13.9%) and 4.0%-44.4% (average 22.3%) than that of the basic model. Model test results showed that, compared with the basic model, the average absolute error (MAE) of mixed effect model was reduced by 0.0001-0.46 m, with an average reduction of 0.08 m, and the average prediction error percentage (MPSE) decreased by 0.1%-6.2%, with an average reduction of 2.0%. The mixed effect model could improve the fitting effect and prediction ability of the model. The ground diameter-height model of seedlings and samplings of main regeneration species in broad-leaved mixed forest was developed in this study, which provides a reference for structure analysis and stand growth prediction of natural broad-leaved forest.

Introduction Bangladesh has made significant strides in maternal and neonatal death by ensuring qualified antenatal care (ANC) visits during the pregnancy period of women. To ensure this qualified ANC, the government of Bangladesh has increased the number of qualified physicians and skilled birth attendants at health facilities and encouraged pregnant women to take this eligible ANC during pregnancy. Despite this progress, the majority of deliveries among rural women still occur at home, assisted by traditional birth attendants. These traditional birth attendants at home or even skilled birth attendants at the health facility or home are not always cable of helping women to overcome severe delivery complications. Proper birth preparation before pregnancy through qualified ANC might be a solution here. Taking advice for appropriate birth preparation from a qualified physician (medical doctor) would ensure qualified ANC. In this study, we examined how ANC from a qualified physician, as compared to other trained providers, influences rural women delivering at home to prepare for birth and reduces severe delivery complications. Methods The data of 1554 rural women who delivered at home were extracted from the 2014 Bangladesh Demographic and Health Survey data. A mixed-effects logistic regression model was carried out for the binary delivery complications data, to assess the influence of qualified physicians as ANC providers on delivery complications by adjusting the effect of other socio-demographic covariates and clustering. Results Of the women from rural areas who delivered at home, 42% reported delivery complications. Those who received ANC from a qualified physician were 32% less likely (OR 0.68; 95% CI 0.50, 0.91) to report facing delivery complications as compared to those who had received ANC from other trained or unqualified providers adjusted by socio-demographic determinants in Bangladesh. Conclusions Developing a sustained and effective strategy could be a precedent for promoting ANC from qualified physicians for rural women delivering at home, to decrease delivery complications as well as creating healthy environments for safe deliveries., Competing Interests: The authors have declared that no competing interests exist.

This study proposes a time-varying effect model that can be used to characterize gender-specific trajectories of health behaviors and conduct hypothesis testing for gender differences. The motivating examples demonstrate that the proposed model is applicable to not only multi-wave longitudinal studies but also short-term studies that involve intensive data collection. The simulation study shows that the accuracy of estimation of trajectory functions improves as the sample size and the number of time points increase. In terms of the performance of the hypothesis testing, the type I error rates are close to their corresponding significance levels under all combinations of sample size and number of time points. Furthermore, the power increases as the alternative hypothesis deviates more from the null hypothesis, and the rate of this increasing trend is higher when the sample size and the number of time points are larger.