Longitudinal associations of plasma amino acid levels with recovery from malarial coma.

BACKGROUND: Disordered amino acid metabolism is observed in cerebral malaria (CM). This study sought to determine whether abnormal amino acid concentrations were associated with level of consciousness in children recovering from coma. Twenty-one amino acids and coma scores were quantified longitudinally and the data were analysed for associations. METHODS: In a prospective observational study, 42 children with CM were enrolled. Amino acid levels were measured at entry and at frequent intervals thereafter and consciousness was assessed by Blantyre Coma Scores (BCS). Thirty-six healthy children served as controls for in-country normal amino acid ranges. Logistic regression was employed using a generalized linear mixed-effects model to assess associations between out-of-range amino acid levels and BCS. RESULTS: At entry 16/21 amino acid levels were out-of-range. Longitudinal analysis revealed 10/21 out-of-range amino acids were significantly associated with BCS. Elevated phenylalanine levels showed the highest association with low BCS. This finding held when out-of-normal-range data were analysed at each sampling time. CONCLUSION: Longitudinal data is provided for associations between abnormal amino acid levels and recovery from CM. Of 10 amino acids significantly associated with BCS, elevated phenylalanine may be a surrogate for impaired clearance of ether lipid mediators of inflammation and may contribute to CM pathogenesis.

Jointly modeling of sleep variables that are objectively measured by wrist actigraphy.

Recently developed actigraphy devices have made it possible for continuous and objective monitoring of sleep over multiple nights. Sleep variables captured by wrist actigraphy devices include sleep onset, sleep end, total sleep time, wake time after sleep onset, number of awakenings, etc. Currently available statistical methods to analyze such actigraphy data have limitations. First, averages over multiple nights are used to summarize sleep activities, ignoring variability over multiple nights from the same subject. Second, sleep variables are often analyzed independently. However, sleep variables tend to be correlated with each other. For example, how long a subject sleeps at night can be correlated with how long and how frequent he/she wakes up during that night. It is important to understand these inter-relationships. We therefore propose a joint mixed effect model on total sleep time, number of awakenings, and wake time. We develop an estimating procedure based upon a sequence of generalized linear mixed effects models, which can be implemented using existing software. The use of these models not only avoids computational intensity and instability that may occur by directly applying a numerical algorithm on a complicated joint likelihood function, but also provides additional insights on sleep activities. We demonstrated in simulation studies that the proposed estimating procedure performed well in estimating both fixed and random effects' parameters. We applied the proposed model to data from the Women's Interagency HIV Sleep Study to examine the association of employment status and age with overall sleep quality assessed by several actigraphy measured sleep variables.

Clinics register based HIV prevalence in Jimma zone, Ethiopia: applications of likelihood and Bayesian approaches.

BACKGROUND: The distribution of HIV is not uniform in Ethiopia with some regions recording higher prevalence than others. However, reported regional HIV prevalence estimates mask the heterogeneity of the epidemic within regions. The main purpose of this study was to assess the district differences in HIV prevalence and other factors that affect the prevalence of HIV infection in Jimma zone, Oromia region of Ethiopia. We aimed to identify districts which had higher or lower than zone average HIV prevalence. Such in-depth analysis of HIV data at district level may help to develop effective strategies to reduce the HIV transmission rate. METHODS: Data collected from 8440 patients who were tested for HIV status in government clinics at the 22 Districts between September 2018 to August 2019 in Jimma zone were used for the analyses. A generalized linear mixed effects model with district random effects was applied to assess the factors associated with HIV infection and the best linear unbiased prediction was used to identify districts that had higher or lower HIV infection. Both likelihood and Bayesian methods were considered. RESULTS: The statistical test on district random effects variance suggested the need for district random effects in all the models. The results from applying both methods on full data show that the odds of HIV infection are significantly associated with covariates considered in this study. Disaggregation of prevalence by gender also highlighted the persistent features of the HIV epidemic in Jimma zone. After controlling for covariates effects, the results from both techniques revealed that there was heterogeneity in HIV infection prevalence among districts within Jimma zone, where some of them had higher and some had lower HIV infection prevalence compared to the zone average HIV infection prevalence. CONCLUSIONS: The study recommends government to give attention to those districts which had higher HIV infection and to conduct further research to improve their intervention strategies. Further, related to those districts which had lower infection, it would be advantageous to identify reasons for their performance and may apply them to overcome HIV infection among residents in those districts which had higher HIV infection. The approach used in this study can also help to assess the effect of interventions introduced by the authorities to control the epidemic and it can easily be extended to assess the regions HIV infection rate relative to the rate at the national level, or zones HIV infection rate relative to the rate at a region level.

Determinants of recommended antenatal care visits among pregnant women in Ethiopia: a generalized linear mixed-effects modeling.

BACKGROUND: Although antenatal care has the potential role to reduce maternal and child morbidity and mortality, utilization of a recommended number of antenatal care visits is still low in Ethiopia. Therefore, this study aimed to assess the determinants of recommended antenatal care visits in Ethiopia. METHOD: Data from the 2019 mini-Ethiopian demographic and health survey (MEDHS) was used for this study. A total of 3916 women who gave birth 5 years preceding the MEDHS were included. A generalized linear mixed-effects (mixed-effects logistic regression) model was used to identify the determinants of recommended antenatal care service utilization. Finally, the adjusted odds ratio with a 95% confidence interval and random effects were reported. RESULTS: In the generalized linear mixed-effects model, women with primary education (AOR = 1.55, 95%CI 1.22-2.01), secondary and above education (AOR = 5.12, 95%CI 2.80-8.16), women from the middle (AOR = 1.25, 95%CI 1.01-1.71) and rich wealth index (AOR = 1.54, 95%CI 1.12-2.25), women who were exposed to media (AOR = 1.23,95%CI 1.01-1.57) and who use contraception (AOR = 1.45 95%CI 1.25-2.03), had higher odds of recommended antenatal care service utilization. CONCLUSION: In this study, factors like maternal educational status, media exposure, wealth index and history of contraceptive utilization were significantly associated with recommended ANC visits in Ethiopia. Therefore, encouraging women for contraceptive service utilization, consulting women to be exposed to media and improving women's wealth status will help to have recommended number of ANC visits by pregnant women in Ethiopia.

Spatial distribution of traditional male circumcision and associated factors in Ethiopia; using multilevel generalized linear mixed effects model.

BACKGROUND: Traditional male circumcision (TMC) is primarily associated with a religious or cultural purpose and may lead to complications. To reduce risks of complication and long-term disabilities that may happen from circumcisions that are undertaken in non-clinical settings, information concerning TMC is very important. Therefore, this study is aimed at identifying spatial distribution of TMC and the factors associated with TMC in Ethiopia. METHODS: A secondary data analysis was conducted among 11,209 circumcised males using data from 2016 Ethiopian Demographic and Health Survey (EDHS). Global Moran's I statistic was observed to check whether there was a significant clustering of TMC. Primary and secondary clusters of TMC were identified by fitting Bernoulli model in Kulldorff's SaTScan software. Multilevel Generalized Linear Mixed effects Model (GLMM) was fitted to identify factors associated with TMC. RESULT: The spatial distribution of TMC was nonrandom across the country with Global Moran's I = 0.27 (p-value < 0.0001). The primary clusters of TMC were identified in the southern part of Oromia and Tigray, northern part of SNNPR, Amhara, Gambella and Benishangul regions. Current age, age at circumcision, ethnicity, religion, place of residence, wealth index, media exposure, sex of household head and age of household head were factors associated with TMC in Ethiopia. CONCLUSIONS: The spatial distribution of TMC was varied across the country. This variation might be due to the diversity of culture, ethnicity and religion across the regions. Thus, there is a need to rearrange the regulations on standards of TMC practice, conduct training to familiarize operation technique and general hygiene procedures, and launch cross-referral systems between traditional circumcisers and health workers. While undertaking these public health interventions, due attention should be given to the identified clusters and significant factors.

Power analysis for RNA-Seq differential expression studies using generalized linear mixed effects models.

BACKGROUND: Power analysis becomes an inevitable step in experimental design of current biomedical research. Complex designs allowing diverse correlation structures are commonly used in RNA-Seq experiments. However, the field currently lacks statistical methods to calculate sample size and estimate power for RNA-Seq differential expression studies using such designs. To fill the gap, simulation based methods have a great advantage by providing numerical solutions, since theoretical distributions of test statistics are typically unavailable for such designs. RESULTS: In this paper, we propose a novel simulation based procedure for power estimation of differential expression with the employment of generalized linear mixed effects models for correlated expression data. We also propose a new procedure for power estimation of differential expression with the use of a bivariate negative binomial distribution for paired designs. We compare the performance of both the likelihood ratio test and Wald test under a variety of simulation scenarios with the proposed procedures. The simulated distribution was used to estimate the null distribution of test statistics in order to achieve the desired false positive control and was compared to the asymptotic Chi-square distribution. In addition, we applied the procedure for paired designs to the TCGA breast cancer data set. CONCLUSIONS: In summary, we provide a framework for power estimation of RNA-Seq differential expression under complex experimental designs. Simulation results demonstrate that both the proposed procedures properly control the false positive rate at the nominal level.

Longitudinal beta-binomial modeling using GEE for overdispersed binomial data.

Longitudinal binomial data are frequently generated from multiple questionnaires and assessments in various scientific settings for which the binomial data are often overdispersed. The standard generalized linear mixed effects model may result in severe underestimation of standard errors of estimated regression parameters in such cases and hence potentially bias the statistical inference. In this paper, we propose a longitudinal beta-binomial model for overdispersed binomial data and estimate the regression parameters under a probit model using the generalized estimating equation method. A hybrid algorithm of the Fisher scoring and the method of moments is implemented for computing the method. Extensive simulation studies are conducted to justify the validity of the proposed method. Finally, the proposed method is applied to analyze functional impairment in subjects who are at risk of Huntington disease from a multisite observational study of prodromal Huntington disease. Copyright © 2016 John Wiley & Sons, Ltd.

Eluxadoline benefits patients with irritable bowel syndrome with diarrhea in a phase 2 study.

BACKGROUND & AIMS: Simultaneous agonism of the µ-opioid receptor and antagonism of the δ-opioid receptor can reduce abdominal pain and diarrhea in patients with irritable bowel syndrome with diarrhea (IBS-D) without constipating side effects. We evaluated the efficacy and safety of a minimally absorbed, µ-opioid receptor agonist and δ-opioid receptor antagonist (eluxadoline) in a phase 2 study in patients with IBS-D. METHODS: We randomly assigned 807 patients to groups that received oral placebo twice daily or 5, 25, 100, or 200 mg oral eluxadoline for 12 weeks. The primary end point was clinical response at week 4, defined by a mean reduction in daily pain score from baseline of ≥ 30%, and of at least 2 points on 0-10 scale, as well as a stool consistency score of 3 or 4 on the Bristol Stool Scale (1-7) for at least 66% of daily diary entries during that week. RESULTS: Significantly more patients receiving 25 mg (12.0%) or 200 mg (13.8%) eluxadoline met the primary end point of clinical response than patients given placebo (5.7%; P < .05). Patients receiving eluxadoline at 100 mg and 200 mg also had greater improvements in bowel movement frequency and urgency, global symptoms, quality of life, and adequate relief assessments (P < .05). Additionally, patients receiving 100 mg (28.0%) or 200 mg (28.5%) eluxadoline were significantly more likely than those receiving placebo (13.8%; P < .005) to meet the US Food and Drug Administration response end point during the full 12 weeks of the study. Eluxadoline was well tolerated with a low incidence of constipation. CONCLUSIONS: In a phase 2 study of the mixed µ-opioid receptor agonist/δ-opioid receptor antagonist eluxadoline vs placebo in patients with IBS-D, patients given eluxadoline were significantly more likely to be clinical responders, based on a composite of improvement in abdominal pain and stool consistency. Further study of eluxadoline is warranted to assess its potential as a treatment for IBS-D.

Role of kicking action in front crawl: the inter-relationships between swimming velocity, hand propulsive force and trunk inclination.

This study aimed to investigate the essential role of the kicking action in front crawl. To achieve this objective, we examined the relationships of the hand propulsive force and trunk inclination with swimming velocity over a wide range of velocities from 0.75 m·s-1 to maximum effort, including the experimental conditions of arm stroke without a pull buoy. Seven male swimmers performed a 25 m front crawl at various speeds under three swimming conditions: arm stroke with a pull buoy, arm stroke without a pull buoy (AWOB) and arm stroke with a six-beat kick (SWIM). Swimming velocity, hand propulsive force and trunk inclination were calculated using an underwater motion-capture system and pressure sensors. Most notably, AWOB consistently exhibited greater values than SWIM for hand propulsive force across the range of observed velocities (p < 0.05) and for trunk inclination below the severe velocity (p < 0.05), and these differences increased with decreasing velocity. These results indicate that 1) the kicking action in front crawl has a positive effect on reducing the pressure drag acting on the trunk, thereby allowing swimmers to achieve a given velocity with less hand propulsive force, and 2) this phenomenon is significant in low-velocity ranges.

Longitudinal associations of plasma amino acid levels with recovery from malarial coma.

Background: Disordered amino acid metabolism is observed in cerebral malaria (CM). We sought to determine whether abnormal amino acid concentrations were associated with level of consciousness in children recovering from coma. We quantified 21 amino acids and coma scores longitudinally and analyzed data for associations. Methods: In a prospective observational study, we enrolled 42 children with CM. We measured amino acid levels at entry and at frequent intervals thereafter and assessed consciousness by Blantyre Coma Scores (BCS). Thirty-six healthy children served as controls for in-country normal amino acid ranges. We employed logistic regression using a generalized linear mixed-effects model to assess associations between out-of-range amino acid levels and BCS. Results: At entry 16/21 amino acid levels were out-of-range. Longitudinal analysis revealed 10/21 out-of-range amino acids were significantly associated with BCS. Elevated phenylalanine levels showed the highest association with low BCS. This finding held when out-of-normal-range data were analyzed at each sampling time. Discussion: We provide longitudinal data for associations between abnormal amino acid levels and recovery from CM. Of 10 amino acids significantly associated with BCS, we propose that elevated phenylalanine may be a surrogate for impaired clearance of ether lipid mediators of inflammation contributing to CM pathogenesis.

The effects of ART on the dynamics of lipid profiles in Chinese Han HIV-infected patients: comparison between NRTI/NNRTI and NRTI/INSTI.

Introduction: This article aimed to compare the prevalence of dyslipidemia and determine risk factors associated with lipid levels in a cohort of HIV-infected patients receiving two different antiretroviral therapy (ART) regimens, nucleoside reverse transcriptase inhibitor/non-nucleoside reverse transcriptase inhibitor (NRTI/NNRTI) and nucleoside reverse transcriptase inhibitor/integrase strand transfer inhibitor (NRTI/INSTI). Methods: This longitudinal study analyzed 633 HIV-infected patients with complete blood lipid profile records for at least 1 year at the ART clinic of Zhongnan Hospital of Wuhan University, China, from June 2018 to March 2021. Demographic and clinical data, including age, gender, body weight, height, current/former/non-smoker, current drinker, diabetes mellitus, hypertension, were extracted from electronic medical records. Laboratory tests included hematology, total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), Lipoprotein(a) and CD4 cell count. The observation duration of this study was a maximum of 33 months. Data comparisons were performed using the Chi-square test, Student's t-test and Mann-Whitney U test. Generalized linear mixed-effects model (GLMM) and value of p < 0.05 were used to determine factors associated with serum lipid profiles. Results: In this study, the effect of the NNRTIs group on the lipid profile over time was mainly an increase in TC and HDL-C, while a decrease in TC/HDL-C and LDL/HDL-C. However, the INSTIs group had higher mean TC and lower HDL-C compared to the NNRTIs group, with significantly increased levels of TC, TG, HDL-C, and LDL-C. In the analysis of dyslipidemia rates, there were significant differences in the prevalence of abnormal TG and TC/HDL-C in HIV-infected patients receiving two different ART regimen groups during different follow-up periods. Dyslipidemia, defined as hypercholesterolemia, hypertriglyceridemia, and low HDL-C, was more prevalent in the INSTIs group, with a higher risk of developing hypertriglyceridemia and a higher TC/HDL-C ratio compared to the NNRTIs group. GLMM analysis suggested significantly higher TG values in the INSTIs group (estimated 0.36[0.10, 0.63], SE 0.14, p = 0.008) compared to the NNRTIs group, even after adjusting for other covariates. In addition, GLMM analysis also showed that age, gender, BMI, CD4 count, and ART duration were associated with dyslipidemia. Conclusion: In conclusion, treatment with both commonly-used ART regimens can increase the mean values of lipid profiles and the risk of dyslipidemia. The findings indicated that TG values were significantly higher in the INSTIs group than in HIV-infected patients receiving the NNRTIs regimens. Longitudinal TG values are independently associated with the clinical types of ART regimens.Clinical Trial Number: ChiCTR2200059861.

Incorporating Functional Response Time Effects into a Signal Detection Theory Model.

Signal detection theory (SDT; Tanner & Swets in Psychological Review 61:401-409, 1954) is a dominant modeling framework used for evaluating the accuracy of diagnostic systems that seek to distinguish signal from noise in psychology. Although the use of response time data in psychometric models has increased in recent years, the incorporation of response time data into SDT models remains a relatively underexplored approach to distinguishing signal from noise. Functional response time effects are hypothesized in SDT models, based on findings from other related psychometric models with response time data. In this study, an SDT model is extended to incorporate functional response time effects using smooth functions and to include all sources of variability in SDT model parameters across trials, participants, and items in the experimental data. The extended SDT model with smooth functions is formulated as a generalized linear mixed-effects model and implemented in the gamm4 R package. The extended model is illustrated using recognition memory data to understand how conversational language is remembered. Accuracy of parameter estimates and the importance of modeling variability in detecting the experimental condition effects and functional response time effects are shown in conditions similar to the empirical data set via a simulation study. In addition, the type 1 error rate of the test for a smooth function of response time is evaluated.

A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance.

Next-generation sequencing (NGS) and metabarcoding approaches are increasingly applied to wild animal populations, but there is a disconnect between the widely applied generalized linear mixed model (GLMM) approaches commonly used to study phenotypic variation and the statistical toolkit from community ecology typically applied to metabarcoding data. Here, we describe the suitability of a novel GLMM-based approach for analyzing the taxon-specific sequence read counts derived from standard metabarcoding data. This approach allows decomposition of the contribution of different drivers to variation in community composition (e.g., age, season, individual) via interaction terms in the model random-effects structure. We provide guidance to implementing this approach and show how these models can identify how responsible specific taxonomic groups are for the effects attributed to different drivers. We applied this approach to two cross-sectional data sets from the Soay sheep population of St. Kilda. GLMMs showed agreement with dissimilarity-based approaches highlighting the substantial contribution of age and minimal contribution of season to microbiota community compositions, and simultaneously estimated the contribution of other technical and biological factors. We further used model predictions to show that age effects were principally due to increases in taxa of the phylum Bacteroidetes and declines in taxa of the phylum Firmicutes. This approach offers a powerful means for understanding the influence of drivers of community structure derived from metabarcoding data. We discuss how our approach could be readily adapted to allow researchers to estimate contributions of additional factors such as host or microbe phylogeny to answer emerging questions surrounding the ecological and evolutionary roles of within-host communities. IMPORTANCE NGS and fecal metabarcoding methods have provided powerful opportunities to study the wild gut microbiome. A wealth of data is, therefore, amassing across wild systems, generating the need for analytical approaches that can appropriately investigate simultaneous factors at the host and environmental scale that determine the composition of these communities. Here, we describe a generalized linear mixed-effects model (GLMM) approach to analyze read count data from metabarcoding of the gut microbiota, allowing us to quantify the contributions of multiple host and environmental factors to within-host community structure. Our approach provides outputs that are familiar to a majority of field ecologists and can be run using any standard mixed-effects modeling packages. We illustrate this approach using two metabarcoding data sets from the Soay sheep population of St. Kilda investigating age and season effects as worked examples.

Meta-analysis methods for risk difference: A comparison of different models.

Risk difference is a frequently-used effect measure for binary outcomes. In a meta-analysis, commonly-used methods to synthesize risk differences include: (1) the two-step methods that estimate study-specific risk differences first, then followed by the univariate common-effect model, fixed-effects model, or random-effects models; and (2) the one-step methods using bivariate random-effects models to estimate the summary risk difference from study-specific risks. These methods are expected to have similar performance when the number of studies is large and the event rate is not rare. However, studies with zero events are common in meta-analyses, and bias may occur with the conventional two-step methods from excluding zero-event studies or using an artificial continuity correction to zero events. In contrast, zero-event studies can be included and modeled by bivariate random-effects models in a single step. This article compares various methods to estimate risk differences in meta-analyses. Specifically, we present two case studies and three simulation studies to compare the performance of conventional two-step methods and bivariate random-effects models in the presence or absence of zero-event studies. In conclusion, we recommend researchers using bivariate random-effects models to estimate risk differences in meta-analyses, particularly in the presence of zero events.

EcoCountHelper: an R package and analytical pipeline for the analysis of ecological count data using GLMMs, and a case study of bats in Grand Teton National Park.

Here we detail the use of an R package, 'EcoCountHelper', and an associated analytical pipeline aimed at making generalized linear mixed-effects model (GLMM)-based analysis of ecological count data more accessible. We recommend a GLMM-based analysis workflow that allows the user to (1) employ selection of distributional forms (Poisson vs negative binomial) and zero-inflation (ZIP and ZINB, respectively) using AIC and variance-mean plots, (2) examine models for goodness-of-fit using simulated residual diagnostics, (3) interpret model results via easy to understand outputs of changes in predicted responses, and (4) compare the magnitude of predictor variable effects via effects plots. Our package uses a series of easy-to-use functions that can accept both wide- and long-form multi-taxa count data without the need for programming experience. To demonstrate the utility of this approach, we use our package to model acoustic bat activity data relative to multiple landscape characteristics in a protected area (Grand Teton National Park), which is threatened by encroaching disease-white nose syndrome. Global threats to bat conservation such as disease and deforestation have prompted extensive research to better understand bat ecology. Notwithstanding these efforts, managers operating on lands crucial to the persistence of bat populations are often equipped with too little information regarding local bat activity to make informed land-management decisions. In our case study in the Tetons, we found that an increased prevalence of porous buildings increases activity levels of Eptesicus fuscus and Myotis volans; Myotis lucifugus activity decreases as distance to water increases; and Myotis volans activity increases with the amount of forested area. By using GLMMs in tandem with 'EcoCountHelper', managers without advanced programmatic or statistical expertise can assess the effects of landscape characteristics on wildlife in a statistically-robust framework.

Dose-Response Effects of Patient Engagement on Health Outcomes in an mHealth Intervention: Secondary Analysis of a Randomized Controlled Trial.

BACKGROUND: The dose-response relationship between patient engagement and long-term intervention effects in mobile health (mHealth) interventions are understudied. Studies exploring long-term and potentially changing relationships between patient engagement and health outcomes in mHealth interventions are needed. OBJECTIVE: This study aims to examine dose-response relationships between patient engagement and 3 psychosocial outcomes in an mHealth intervention, Run4Love, using repeated measurements of outcomes at baseline and 3, 6, and 9 months. METHODS: This study is a secondary analysis using longitudinal data from the Run4Love trial, a randomized controlled trial with 300 people living with HIV and elevated depressive symptoms to examine the effects of a 3-month mHealth intervention on reducing depressive symptoms and improving quality of life (QOL). We examined the relationships between patient engagement and depressive symptoms, QOL, and perceived stress in the intervention group (N=150) using 4-time-point outcome measurements. Patient engagement was assessed using the completion rate of course assignments and frequency of items completed. Cluster analysis was used to categorize patients into high- and low-engagement groups. Generalized linear mixed effects models were conducted to investigate the dose-response relationships between patient engagement and outcomes. RESULTS: The cluster analysis identified 2 clusters that were distinctively different from each other. The first cluster comprised 72 participants with good compliance to the intervention, completing an average of 74% (53/72) of intervention items (IQR 0.22). The second cluster comprised 78 participants with low compliance to the intervention, completing an average of 15% (11/72) of intervention items (IQR 0.23). Results of the generalized linear mixed effects models showed that, compared with the low-engagement group, the high-engagement group had a significant reduction in more depressive symptoms (ß=-1.93; P=.008) and perceived stress (ß=-1.72; P<.001) and an improved QOL (ß=2.41; P=.01) over 9 months. From baseline to 3, 6, and 9 months, the differences in depressive symptoms between the 2 engagement groups were 0.8, 1.6, 2.3, and 3.7 points, respectively, indicating widening between-group differences over time. Similarly, between-group differences in QOL and perceived stress increased over time (group differences in QOL: 0.9, 1.9, 4.7, and 5.1 points, respectively; group differences in the Perceived Stress Scale: 0.9, 1.4, 2.3, and 3.0 points, respectively). CONCLUSIONS: This study revealed a positive long-term dose-response relationship between patient engagement and 3 psychosocial outcomes among people living with HIV and elevated depressive symptoms in an mHealth intervention over 9 months using 4 time-point repeat measurement data. The high- and low-engagement groups showed significant and widening differences in depressive symptoms, QOL, and perceived stress at the 3-, 6-, and 9-month follow-ups. Future mHealth interventions should improve patient engagement to achieve long-term and sustained intervention effects. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR-IPR-17012606; https://www.chictr.org.cn/showproj.aspx?proj=21019.

Generalized linear mixed-effects models for studies using different sets of stimuli across conditions.

A non-repeated item (NRI) design refers to an experimental design in which items used in one level of experimental conditions are not repeatedly used at other levels. Recent literature has suggested the use of generalized linear mixed-effects models (GLMMs) for experimental data analysis, but the existing specification of GLMMs does not account for all possible dependencies among the outcomes in NRI designs. Therefore, the current study proposed a GLMM with a level-specific item random effect for NRI designs. The hypothesis testing performance of the newly proposed model was evaluated via a simulation study to detect the experimental condition effect. The model with a level-specific item random effect performed better than the existing model in terms of power when the variance of the item effect was heterogeneous. Based on these results, we suggest that experimental researchers using NRI designs consider setting a level-specific item random effect in the model.

Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research.

In basic neuroscience research, data are often clustered or collected with repeated measures, hence correlated. The most widely used methods such as t test and ANOVA do not take data dependence into account and thus are often misused. This Primer introduces linear and generalized mixed-effects models that consider data dependence and provides clear instruction on how to recognize when they are needed and how to apply them. The appropriate use of mixed-effects models will help researchers improve their experimental design and will lead to data analyses with greater validity and higher reproducibility of the experimental findings.

A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in Canada.

During a pandemic, data are very "noisy" with enormous amounts of local variation in daily counts, compared with any rapid changes in trend. Accurately characterizing the trends and reliable predictions on future trajectories are important for planning and public situation awareness. We describe a semi-parametric statistical model that is used for short-term predictions of daily counts of cases and deaths due to COVID-19 in Canada, which are routinely disseminated to the public by Public Health Agency of Canada. The main focus of the paper is the presentation of the model. Performance indicators of our model are defined and then evaluated through extensive sensitivity analyses. We also compare our model with other commonly used models such as generalizations of logistic models for similar purposes. The proposed model is shown to describe the historical trend very well with excellent ability to predict the short-term trajectory.

Watershed farmland area and instream water quality co-determine the stream primary producer in the central Hengduan Mountains, southwestern China.

As the main primary producer in stream ecosystems, periphyton is the fundamental of stream ecosystems and plays an essential role in maintaining stream biodiversity. The central Hengduan Mountains is one of the famous global biodiversity hotspots. However, for stream biodiversity conservations, the fundamental information about the spatial pattern of stream periphyton and the determining factors in this region remains largely unknown. To fill this gap, we investigated the spatial pattern of periphyton biomass (measured by chlorophyll a) in four typical streams in the central Hengduan Mountains and analyzed the driving factors, with the perspective of watershed land use and instream water quality. The results of our study found that farmland area, instream dissolved silicate (DSi), and water temperature (WT) had significant negative relationships with the stream periphyton biomass in the central Hengduan Mountains. However, the dissolved inorganic phosphorus (DIP) and dissolved inorganic nitrogen (DIN) had non-significant effects on the periphyton biomass in our study. Further analysis showed that the correlation between the ratio of farmland in the catchment and the DSi is non-significant (r = 0.028, P = 0.698), suggesting that the changes in farmland area will not affect the concentration of dissolved silicate in the stream. This reveals that the stream periphyton biomass was co-determined by the watershed farmland area and instream DSi and WT, but not nitrogen or phosphorus. These results highlight the importance of farmland management and instream DSi for stream ecosystems in the central Hengduan Mountains. Our study investigated the spatial pattern of the stream primary producer in the central Hengduan Mountains region and identified the main determining factors, which could improve our understanding of the high mountain stream ecosystems.