Linear Mixed Effects (LME) models are powerful statistical tools that have been employed in many different real-world applications such as retail data analytics, marketing measurement, and medical research. Statistical inference is often conducted via maximum likelihood estimation with Normality assumptions on the random effects. Nevertheless, for many applications in the retail industry, it is often necessary to consider non-Normal distributions on the random effects when considering the unknown parameters\' business interpretations. Motivated by this need, a linear mixed effects model with possibly non-Normal distribution is studied in this research. We propose a general estimating framework based on a saddlepoint approximation (SA) of the probability density function of the dependent variable, which leads to constrained nonlinear optimization problems. The classical LME model with Normality assumption can then be viewed as a special case under the proposed general SA framework. Compared with the existing approach, the proposed method enhances the real-world interpretability of the estimates with satisfactory model fits.

Joint models for longitudinal and time-to-event data are receiving increasing attention owing to its capability of capturing the possible association between these two types of data. Typically, a joint model consists of a longitudinal submodel for longitudinal processes and a survival submodel for the time-to-event response, and links two submodels by common covariates that may carry both fixed and random effects. However, research gaps still remain on how to simultaneously select fixed and random effects from the two submodels under the joint modeling framework efficiently and effectively. In this article, we propose a novel block-coordinate gradient descent (BCGD) algorithm to simultaneously select multiple longitudinal covariates that may carry fixed and random effects in the joint model. Specifically, for the multiple longitudinal processes, a linear mixed effect model is adopted where random intercepts and slopes serve as essential covariates of the trajectories, and for the survival submodel, the popular proportional hazard model is employed. A penalized likelihood estimation is used to control the dimensionality of covariates in the joint model and estimate the unknown parameters, especially when estimating the covariance matrix of random effects. The proposed BCGD method can successfully capture the useful covariates of both fixed and random effects with excellent selection power, and efficiently provide a relatively accurate estimate of fixed and random effects empirically. The simulation results show excellent performance of the proposed method and support its effectiveness. The proposed BCGD method is further applied on two real data sets, and we examine the risk factors for the effects of different heart valves, differing on type of tissue, implanted in the aortic position and the risk factors for the diagnosis of primary biliary cholangitis.

In psychophysics and psychometrics, an integral method to the discipline involves charting how a person\'s response pattern changes according to a continuum of stimuli. For instance, in hearing science, Visual Analog Scaling tasks are experiments in which listeners hear sounds across a speech continuum and give a numeric rating between 0 and 100 conveying whether the sound they heard was more like word \"a\" or more like word \"b\" (i.e. each participant is giving a continuous categorization response). By taking all the continuous categorization responses across the speech continuum, a parametric curve model can be fit to the data and used to analyze any individual\'s response pattern by speech continuum. Standard statistical modeling techniques are not able to accommodate all of the specific requirements needed to analyze these data. Thus, Bayesian hierarchical modeling techniques are employed to accommodate group-level non-linear curves, individual-specific non-linear curves, continuum-level random effects, and a subject-specific variance that is predicted by other model parameters. In this paper, a Bayesian hierarchical model is constructed to model the data from a Visual Analog Scaling task study of mono-lingual and bi-lingual participants. Any nonlinear curve function could be used and we demonstrate the technique using the 4-parameter logistic function. Overall, the model was found to fit particularly well to the data from the study and results suggested that the magnitude of the slope was what most defined the differences in response patterns between continua.

BACKGROUND: Although safety climate, teamwork, and other non-technical skills in operating rooms probably influence clinical outcomes, direct associations have not been shown, at least partially due to sample size considerations. We report data from a retrospective cohort of anesthesia evaluations that can simplify the design of prospective observational studies in this area. Associations between non-technical skills in anesthesia, specifically anesthesiologists\' quality of clinical supervision and nurse anesthetists\' work habits, and patient and operational factors were examined.
METHODS: Eight fiscal years of evaluations and surgical cases from one hospital were included. Clinical supervision by anesthesiologists was evaluated daily using a nine-item scale. Work habits of nurse anesthetists were evaluated daily using a six-item scale. The dependent variables for both groups of staff were binary, whether all items were given the maximum score or not. Associations were tested with patient and operational variables for the entire day.
RESULTS: There were 40,718 evaluations of faculty anesthesiologists by trainees, 53,772 evaluations of nurse anesthetists by anesthesiologists, and 296,449 cases that raters and ratees started together. Cohen\'s d values were small (≤0.10) for all independent variables, suggesting a lack of any clinically meaningful association between patient and operational factors and evaluations given the maximum scores. For supervision quality, the day\'s count of orthopedic cases was a significant predictor of scores (P = 0.0011). However, the resulting absolute marginal change in the percentage of supervision scores equal to the maximum was only 0.8% (99% confidence interval: 0.2% to 1.4%), i.e., too small to be of clinical or managerial importance. Neurosurgical cases may have been a significant predictor of work habits (P = 0.0054). However, the resulting marginal change in the percentage of work habits scores equal to the maximum, an increase of 0.8% (99% confidence interval: 0.1% to 1.6%), which was again too small to be important.
CONCLUSIONS: When evaluating the effect of assigning anesthesiologists and nurse anesthetists with different clinical performance quality on clinical outcomes, supervision quality and work habits scores may be included as independent variables without concern that their effects are confounded by association with the patient or case characteristics. Clinical supervision and work habits are measures of non-technical skills. Hence, these findings suggest that non-technical performance can be judged by observing the typical small sample size of cases. Then, associations can be tested with administrative data for a far greater number of patients because there is unlikely to be a confounding association between patient and case characteristics and the clinicians\' non-technical performance.

The purpose of this study was to investigate the effects of problem-solving pedagogy on individual students\' creativity in different teaching contexts and conditions, and to examine the role of moderating variables that may affect the overall effect size. The study screened 19 relevant studies from the Web of Science for inclusion in the meta-analysis, and extracted 77 effect sizes from these studies that could be used in the meta-analysis. The study first explored the relationship between problem-solving pedagogy and the development of learner creativity, and further analysis focused on moderating variables to investigate the effects of instructional method, grouping method, grade level, problem-solving scaffolding, source of problems, the application of thinking tools, and the application of technology. The results showed that problem-solving pedagogy enhances students\' creativity, while at the same time, among the sources of problems, student-discovered problems are most conducive to creativity stimulation, while teacher-determined problems and problems that lead to student discovery are less effective in terms of promoting creativity. Among the grouping methods, heterogeneous grouping can better utilize the creativity cultivation effect of the problem-solving pedagogy than random grouping and homogeneous grouping. Among the different levels of grades, learners in elementary school are better able to gain creativity under the influence of problem-solving pedagogy than learners in middle school, high school, college, and those in on-the-job learning. However, this study did not reveal any significant benefits for creativity enhancement under the four conditions of instructional method, problem-solving scaffolding, thinking tools-assisted, or technology. The results of this study clarify the importance of problem-solving pedagogy for creativity development, and also reveal the actual effects of the various ways of applying problem-solving pedagogy on creativity development as well as the problems that exist, which provides inspiration for how to better utilize problem-solving pedagogy in the future.

UNASSIGNED: The coronavirus disease 2019 (COVID-19) pandemic has been a global health crisis and continues to pose risk to population health at the present. Vaccination against this disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has become a public health priority worldwide. Yet, limited information is available on the potential impact of such vaccines on human fertility.
UNASSIGNED: To examine the relationship between COVID-19 vaccination and male fertility, we conducted an observational study on sperm donor candidates in China who received Chinese COVID-19 vaccines between January 1, 2020 to December 31, 2021.
UNASSIGNED: A total of 2,955 semen samples from 564 individuals were assessed along with vaccination information. Statistical analyses were conducted on both the entire study population and the subgroup of individuals who provided repeated semen samples before and after vaccination. While motility related parameters [progressive rate, curvilinear velocity (VCL), average path velocity (VAP), straight-line velocity (VSL), wobble (WOB), straightness (STR), linearity (LIN), amplitude of lateral head displacement (ALH), beat-cross frequency (BCF)] exhibited statistically significant difference before and after vaccination based on Welch two-sample test, mixed effects regression results based on repeated measures from the same individuals indicated that vaccination was not statistically associated with sperm quality parameters except for VCL, VAP, and VSL. Individual variability was the key determinant of sperm quality variance, with contribution ranging from 19% to 82%.
UNASSIGNED: Findings from our study could help to enhance current understanding of male reproductive health in the context of the global pandemic.

Currently few studies have explored the relationship between exposure to gaseous pollutants and metabolic health indicators in patients, especially in patients with metabolic syndrome (Mets). This study collected 15,520 patients with Mets in a prospective cohort of nearly 50,000 people with 7 years of follow-up from 2011 to 2017, and matched air pollutants and meteorological data during the same period. The mixed effects model was used to analyze the relationship between different short exposure windows (1-week, 1-month, 2-month, and 3-month) of gaseous pollutants (SO2, NO2, and O3) and the metabolic health indicators of patients after controlled the confounding factors. Stratified analysis was performed by demographic characteristics and behavioral factors. The effects of gaseous pollutants on patients with different Met components were also analyzed. The results showed that the short-term exposure to SO2, NO2, and O3 had a certain effect on the metabolic health indicators of patients with Mets in different exposure windows, and with the extension of the exposure window period, the effects increased. The stratified analysis showed that gender, age, and life behaviors might modify these detrimental effects. In addition, the effects of gaseous pollutants on metabolic health indicators in G4 and G7 were more obvious than other Met components, and the effects of gaseous pollutants on the level of LDL-C were found to be statistically significant in most components. Therefore, patients with Mets should pay more attention to the influence of gaseous pollutants to take appropriate protection to reduce potential health risk.

The iodine balance experiment is a traditional approach to evaluate the physiological requirement for iodine, while the simple linear regression model (SLM) and the mixed effects model (MEM) are two primary methods used to analyze iodine balance experiments. In the present study, we aimed to compare the effects of these two regression models on the evaluation of iodine balance experiments to investigate appropriate valuation methods. By constructing SLM and MEM, zero iodine balance values (IBV) were determined, and the evaluation effects were compared. No changes were made to the experimental data for women of childbearing age, and cutoff values of 600 µg/day and 1000 µg/day, respectively, were chosen for further processing of the experimental data for pregnant women. Equation combinations 1-3 (EC1-3) were obtained by fitting SLM, and zero IBV were calculated as 110.26 µg/day, 333.06 µg/day, and 434.84 µg/day, respectively. EC4-6 were obtained by fitting MEM, and zero IBV were calculated as 110.44 µg/day, 335.79 µg/day, and 418.06 µg/day, respectively. The inclusion of inter-measurement variation as a random factor in the MEM yielded EC7-8, which reduced the test power of the iodine balance experiment on women of childbearing age. Our study suggested that when experimental conditions were tightly controlled, with fewer uncertainties or significant influences, computationally straightforward and well-understood SLM was preferred. If some uncertain factors might cause large changes in the experimental results, it was advised to use a more \"conservative\" MEM to calculate the zero IBV. ClinicalTrials.gov Identifier: Registered at Clinicaltrials.gov, NCT03279315 (17th September 2017, retrospectively registered), NCT03710148 (18th October 2018, retrospectively registered).

Classifying patient biomarker trajectories into groups has become frequent in clinical research. Mixed effects classification models can be used to model the heterogeneity of longitudinal data. The estimated parameters of typical trajectories and the partition can be provided by the classification version of the expectation maximization algorithm, named CEM. However, the variance of the parameter estimates obtained underestimates the true variance because classification uncertainties are not taken into account. This article takes into account these uncertainties by using the stochastic EM algorithm (SEM), a stochastic version of the CEM algorithm, after convergence of the CEM algorithm. The simulations showed correct coverage probabilities of the 95% confidence intervals (close to 95% except for scenarios with high bias in typical trajectories). The method was applied on a trial, called low-cyclo, that compared the effects of low vs standard cyclosporine A doses on creatinine levels after cardiac transplantation. It identified groups of patients for whom low-dose cyclosporine may be relevant, but with high uncertainty on the dose-effect estimate.

As human complex diseases are influenced by the interplay of genes and environment, detecting gene-environment interactions (G×E) can shed light on biological mechanisms of diseases and play an important role in disease risk prediction. Development of powerful quantitative tools to incorporate G×E in complex diseases has potential to facilitate the accurate curation and analysis of large genetic epidemiological studies. However, most of existing methods that interrogate G×E focus on the interaction effects of an environmental factor and genetic variants, exclusively for common or rare variants. In this study, we proposed two tests, MAGEIT_RAN and MAGEIT_FIX, to detect interaction effects of an environmental factor and a set of genetic markers containing both rare and common variants, based on the MinQue for Summary statistics. The genetic main effects in MAGEIT_RAN and MAGEIT_FIX are modeled as random or fixed, respectively. Through simulation studies, we illustrated that both tests had type I error under control and MAGEIT_RAN was overall the most powerful test. We applied MAGEIT to a genome-wide analysis of gene-alcohol interactions on hypertension in the Multi-Ethnic Study of Atherosclerosis. We detected two genes, CCNDBP1 and EPB42, that interact with alcohol usage to influence blood pressure. Pathway analysis identified sixteen significant pathways related to signal transduction and development that were associated with hypertension, and several of them were reported to have an interactive effect with alcohol intake. Our results demonstrated that MAGEIT can detect biologically relevant genes that interact with environmental factors to influence complex traits.