Network-Level Modeling of Pavement Surface Macrotexture Degradation Using Linear Mixed-Effects Models

BackgroundAmong persons living with HIV (PLWH) with type 2 diabetes mellitus (DM) there is limited research on the effect of DM control on CD4 count. Current guidelines recommend that PLWH with DM maintain a hemoglobin A1c (HbA1c) <7%. This analysis examined the impact of HbA1c on trends in CD4 count among PLWH receiving care in Washington, DC.MethodsWe used data from the DC Cohort, a longitudinal observational cohort of patients receiving HIV care at 14 clinics between 2011–2018. Participants with DM on an ongoing antiretroviral regimen with ≥1 year of follow-up, ≥2 HbA1c results, and ≥2 CD4 count results were included. Participants were compared based on the most recent HbA1c result categorized into one of three control levels control: strict, HbA1c < 7.5%; moderate, HbA1c between 7.5–9.0%; and uncontrolled, HbA1c >9.0%. All statistical tests were performed within the framework of the linear mixed-effects (LME) model. The rates of increase in CD4 count by DM control were compared using an LME model with random slopes and random intercepts, adjusted for sex, BMI, nadir CD4, a history of AIDS, or cancer diagnosis.ResultsAmong 554 participants (median age 53.5; 70.8% male; 82.7% Black), there were 5,138 total CD4 count measurements. In unadjusted analysis, participants with moderate or uncontrolled HbA1c had higher mean CD4 counts over the follow-up period than those with strict HbA1c control (strict: 690 cells/μL, moderate: 712 cells/μL uncontrolled: 711 cells/μL; P = 0.0156 strict vs. moderate, 0.049 strict vs. uncontrolled). All DM control groups had a similar temporal increase over time in CD4 count (P = 0.46). In multivariate analysis, only moderate vs. strict control showed a significant difference in CD4 count (mean difference=18.1; P = 0.02). Results showed CD4 count change was not affected by the duration of HIV diagnosis or diabetes diagnosis. See Table 1 for additional results.ConclusionPLWH and DM with moderate HbA1c control had higher CD4 counts than those with strict HbA1c control and similar CD4 counts compared with those with uncontrolled HbA1c levels, while the rate of increase in CD4 count was similar in the three groups. These results show that moderate DM control may benefit CD4 count, which should be considered when revising DM control guidelines for PLWH.DisclosuresAll authors: No reported disclosures.

In this study, we investigated the role of climatic variability and atmospheric nitrogen deposition in driving long-term tree growth in canopy beech trees along a geographic gradient in the montane belt of the Italian peninsula, from the Alps to the southern Apennines. We sampled dominant trees at different developmental stages (from young to mature tree cohorts, with tree ages spanning from 35 to 160years) and used stem analysis to infer historic reconstruction of tree volume and dominant height. Annual growth volume (GV ) and height (GH ) variability were related to annual variability in model simulated atmospheric nitrogen deposition and site-specific climatic variables, (i.e. mean annual temperature, total annual precipitation, mean growing period temperature, total growing period precipitation, and standard precipitation evapotranspiration index) and atmospheric CO2 concentration, including tree cambial age among growth predictors. Generalized additive models (GAM), linear mixed-effects models (LMM), and Bayesian regression models (BRM) were independently employed to assess explanatory variables. The main results from our study were as follows: (i) tree age was the main explanatory variable for long-term growth variability; (ii) GAM, LMM, and BRM results consistently indicated climatic variables and CO2 effects on GV and GH were weak, therefore evidence of recent climatic variability influence on beech annual growth rates was limited in the montane belt of the Italian peninsula; (iii) instead, significant positive nitrogen deposition (Ndep ) effects were repeatedly observed in GV and GH ; the positive effects of Ndep on canopy height growth rates, which tended to level off at Ndep values greater than approximately 1.0gm-2 y-1 , were interpreted as positive impacts on forest stand above-ground net productivity at the selected study sites.

Irregular visual field test frequency at relatively short intervals initially and longer intervals later on in the disease provided acceptable results in detecting glaucoma progression. It is challenging to maintain a balance between the frequency of visual field testing and the long-term costs that may result from insufficient treatment of glaucoma patients. This study aims to simulate real-world circumstances of visual field data to determine the optimum follow-up scheme for the timely detection of glaucoma progression using a linear mixed effects model (LMM). An LMM with random intercept and slope was used to simulate the series of mean deviation sensitivities over time. A cohort study including 277 glaucoma eyes that were followed for 9.0±1.2 years was used to derive residuals. Data were generated from patients with early-stage glaucoma having various regular and irregular follow-up scenarios and different rates of visual field loss. For each condition, 10,000 series of eyes were simulated, and one confirmatory test was conducted to identify progression. By doing one confirmatory test, the percentage of incorrect progression detection decreased considerably. The time to detect progression was shorter for eyes with an evenly spaced 4-monthly schedule, particularly in the first 2 years. From then onward, results from twice-a-year testing were similar to results from examinations scheduled 3 times per year. Irregular visual field test frequency at relatively short intervals initially and longer intervals later on in the disease provided acceptable results in detecting glaucoma progression. This approach could be considered for improving glaucoma monitoring. Moreover, simulating data using LMM may provide a better estimate of the disease progression time.

• Hyperglycemia leads to more cautious driving in T1DM, with no impact on T2DM speed limit adherence. • Hypoglycemia prompts T1DM drivers to reduce their speed in extreme situations, with no impact on T2DM speed limit adherence. • Control group drivers exceed speed limits more frequently than those with diabetes, especially T2DM. • Age significantly influences speed limit adherence, while sex does not affect T1DM or T2DM drivers. • Linear Quantile Mixed Effects Model reveals variation in speed adherence during acute glycemic episodes, accounting for participant heterogeneity and highlighting diabetes’ impact on driving. Diabetes can cause complications from hypoglycemic and hyperglycemic episodes, impairing cognitive and motor skills essential for safe driving. Advances in low-cost sensors and wearable technologies have enabled naturalistic driving studies (NDS) that simulate real-world conditions while monitoring drivers’ blood glucose levels. This paper analyzes data from an NDS conducted in Nebraska, focusing on how drivers with Type 1 Diabetes Mellitus (T1DM) and Type 2 Diabetes Mellitus (T2DM), as well as control participants without diabetes, adhere to speed limits of 50–75 mph on highways. Alongside a conventional Linear Mixed Effects Model (LMM), we introduce a novel Linear Quantile Mixed Effects Model (LQMM) to evaluate six quantiles (τ = 0.10, 0.25, 0.50, 0.75, 0.85, and 0.90) of speed limit adherence during acute glucose episodes, including hyperglycemia and hypoglycemia. Findings show that hypoglycemia generally leads T1DM drivers to drive more cautiously and remain below speed limits. No significant effects of hypoglycemia or hyperglycemia were observed on T2DM drivers’ speed adherence, suggesting glycemic fluctuations may not substantially influence their behavior. Hyperglycemia was linked to increased caution among T1DM drivers, consistent with evidence of heightened physiological awareness in this group. Control drivers exceeded speed limits more often than those with diabetes, especially relative to T2DM drivers. Roadway characteristics (e.g., traffic flow and speed limits) and age also influence speed behavior, highlighting important contextual factors. By utilizing distribution-based methods like LQMMs that account for participant heterogeneity, this paper presents a nuanced view of speed control patterns, yielding new insights into how diabetes affects driving safety.

BackgroundLongitudinal performance tracking in sports science is crucial for accurate talent identification and prognostic prediction of future performance. However, traditional methods often struggle with the complexities of unbalanced datasets and inconsistent repeated measures.PurposeThis study aimed to analyze the longitudinal performance development of female 60 m sprint runners using linear mixed effects models (LMM). We sought to generate a practical tool for coaches and researchers to establish benchmarks and predict performance development.MethodsWe analyzed 41,123 race results from 8,732 female 60 m track sprinters aged 6–15 years, collected from the Swiss Athletics online database between 2006 and 2021. Only season-best times per athlete and only athletes with at least 3 season-best times in their career were included. LMM was used to generate performance trajectories, benchmarks, and individual predictions. A practical software tool was developed and made available to allow individual performance prediction based on race times from previous seasons. In addition, classic empirical percentile curves were constructed using the Lambda-Mu-Sigma (LMS) method.ResultsLMM handled the dataset's complexities, producing robust longitudinal performance trajectories. Compared to empirical percentiles generated using the LMS method, which provided a retrospective view of performance development, the mixed model approach identified individualized longitudinal performance developments and estimated predictions of future performance. The best-fitting model included log-transformed chronological age (CA) as a fixed effect and random intercepts and slopes for each athlete. This model explained 59% of the variance through fixed effects (marginal R2) and 93% through combined fixed and random effects (conditional R2).ConclusionLMM provided longitudinal sport performance data, enabling the establishment of performance benchmarking and prediction of future performance. The software tool can assist coaches in setting realistic training goals and identifying promising athletes.

BackgroundPreventive strategies for musculoskeletal disorders require a better understanding of age-related changes in muscle strength, physical function and body composition (including bone). Many publications use simple change measures from observations...

The aim of this study was to examine whether it is possible to gradually increase heart rate variability (HRV) in healthy individuals (21 participants, M = 21.24 years, SD = 1.57, range 19 to 26) through regular exercises of average resonance frequency training (RFT; 6 breaths/min; 5 min each day) and progressive muscle relaxation (PMR; three times a week for 20 min). The effects were tested against an active control group using a linear mixed effect model with random slopes (day), random intercepts (participants) and an autoregressive error term. The special feature of this pilot study is that HRV was measured every day in an ambulatory assessment over 77 days, so that graduate long-term effects on HRV can be mapped. The results indicated that the PMR group significantly increased their HRV compared to the active control group. However, no effect was observed for the RFT group. Possible explanations for these results and important recommendations for subsequent studies are provided.

BackgroundThe Patagonian region hosts endemic bat species and represents the southernmost distribution limit for several vespertilionids and molossids species. In cold temperate regions, insectivorous bats are more active during summer. However, during this period, the activity of bats can also vary spatially, depending on climatic and environmental factors (e.g., temperature, humidity, vegetation cover, productivity, elevation, proximity to water). The objective of this study was to analyze how the spatial activity of phonic groups is affected by climatic and environmental variables in a large, heterogeneous area of ​​Patagonia, Argentina, using bioacoustic methods. Acoustic monitoring was conducted during the austral summer of 2020, at 100 points located at ten sites, in three ecoregions of Chubut Province (Patagonian Forest, Patagonian Steppe and Low Monte). Bat passes were classified into four phonic groups (PGs), each representing species with similar echolocation call structures. This classification was based on foraging habits and bioacoustic characteristics of species commonly recorded in the study area (PG1 = Myotis chiloensis, M. levis; PG2 = Lasiurus varius, L. villosissimus, Histiotus magellanicus; PG3 = H. macrotus, H. montanus; PG4 = Tadarida brasiliensis). The values of eleven variables were obtained for each point (e.g., temperature, relative humidity, vegetation cover, productivity, elevation, and proximity to water). Using generalized linear mixed-effects models (GLMMs), we analyzed how climatic and environmental variables influenced the spatial activity of Patagonian bat phonic groups.ResultsOur results showed that spatial activity of four phonic groups analyzed in summer is driven by environmental (vegetation cover, elevation and proximity to water) and climatic variables (temperature and relative humidity). Nevertheless, the spatial activity of each specific phonic group was mainly influenced by vegetation cover variables and by their preference for each ecoregion, reflecting the habitat structure in which they forage.ConclusionsThe spatial activity of four phonic groups from Central Patagonia in summer is governed jointly by climatic and environmental variables, with vegetation structure being the dominant driver. In the context of climate change, habitat loss and reduced water availability (especially in arid and semi-arid environments) could impact the populations of Patagonian bats, considering the importance of these factors in influencing their spatial activity.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12862-025-02430-7.

One of the key tools to understand and reduce the spread of the SARS-CoV-2 virus is testing. The total number of tests, the number of positive tests, the number of negative tests, and the positivity rate are interconnected indicators and vary with time. To better understand the relationship between these indicators, against the background of an evolving pandemic, the association between the number of positive tests and the number of negative tests is studied using a joint modeling approach. All countries in the European Union, Switzerland, the United Kingdom, and Norway are included in the analysis. We propose a joint penalized spline model in which the penalized spline is reparameterized as a linear mixed model. The model allows for flexible trajectories by smoothing the country-specific deviations from the overall penalized spline and accounts for heteroscedasticity by allowing the autocorrelation parameters and residual variances to vary among countries. The association between the number of positive tests and the number of negative tests is derived from the joint distribution for the random intercepts and slopes. The correlation between the random intercepts and the correlation between the random slopes were both positive. This suggests that, when countries increase their testing capacity, both the number of positive tests and negative tests will increase. A significant correlation was found between the random intercepts, but the correlation between the random slopes was not significant due to a wide credible interval.

Vertical body position during swimming is assumed to closely affect drag. It is consequently associated with swimming velocity; however, the association between swimming velocity and vertical body position has not yet been sufficiently established. Here, we aimed to clarify how vertical body position increases with front crawl velocity and whether there are inter-individual differences in velocity effect. Eleven college-level male swimmers performed a 15 m front crawl with sustained forced maximal inspiration at various swimming velocities. The body’s centre of mass (CoM) was estimated from individual digital human models with inertial parameters using inverse kinematics. The horizontal CoM velocity and vertical CoM position from the water surface were averaged for one stroke cycle as respective indexes of swimming velocity and vertical body position. Linear mixed-effects model analysis revealed that there is a positive trend between swimming velocity and vertical CoM position during front crawl across the participants. These results indicate that swimming velocity is associated with vertical body position during front crawl. Additionally, the linear mixed-effects model with random intercepts and slopes was a better fit than that with only random intercepts, indicating that there are inter-individual differences in the rate of increase in vertical body position against swimming velocity.

The forest dynamics are usually explained by the precipitation and temperature through fixed effects models using ordinary least squares and geographically weighted regression methods. However, forest dynamics were found insufficiently explained by meteorological factors as the fixed effects models were not designed to account for random effects. In this study, we utilized three types of forests located in the Gulf of Mexico Coast region, including softwood, hardwood, and mixed forests to investigate the underlying forest dynamics to meteorological variations by incorporating random effects into fixed effects models. Four types of linear mixed effects models (LMMs) were developed for regressing the normalized difference of vegetation index (NDVI) against two explanatory variables: precipitation and temperature. By assuming that the intercept and slope parameters estimated from LMMs would vary randomly, we intended to explore if the amount of variation in the NDVI variables could be reduced by the use of random effects variables. The results suggested that the random intercept and random slope model fitted the data better than the random intercept model with higher R 2, lower Akaike information criterion, and Bayesian information criterion values. The R 2 value indicated that the explanatory power of the LMM varies between forest types. Moreover, this study revealed that a linear mixed effects model could significantly reduce the unexplained variance by introducing random effects variables, and forest dynamics is a synthetic result of the mixed effects of temperature and fixed effects of precipitation.

Many statistical models have been proposed in the literature for the analysis of longitudinal data. One may propose to model two or more correlated longitudinal processes simultaneously, with a goal of understanding their association over time. Joint modeling is then required to carefully study the association structure among the outcomes as well as drawing joint inferences about the different outcomes. In this study, we sought to model the associations among six nutrition outcomes while circumventing the computational challenge posed by their clustered and high-dimensional nature. We analyzed data from a 2 2 randomized crossover trial conducted in Kenya, to compare the effect of high-dose and low-dose iodine in household salt on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in women of reproductive age and their household matching pair of school-aged children. Two additional outcomes, namely, urinary iodine concentration (UIC) in women and children were measured repeatedly to monitor the amount of iodine excreted through urine. We extended the model proposed by Mwangi etal. (2021, Communications in Statistics: Case Studies, Data Analysis and Applications, 7(3), 413-431) allowing flexible piecewise joint models for six outcomes to depend on separate random effects, which are themselves correlated. This entailed fitting 15 bivariate general linear mixed models and deriving inference for the joint model using pseudo-likelihood theory. We analyzed the outcomes separately and jointly using piecewise linear mixed-effects (PLME) model and further validated the results using current state-of-the-art Jones and Kenward methodology (JKME model) used for analyzing randomized crossover trials. The results indicate that high-dose iodine in salt significantly reduced blood pressure (BP) compared to low-dose iodine in salt. Estimates for the random effects and residual error components showed that SBP and DBP had strong positive correlation, with effect of the random slope indicating that significantly related outcomes are strongly associated in their evolution. There was a moderately strong inverse relationship between evolutions of UIC and BP both in women and children. These findings confirmed the original hypothesis that high-dose iodine salt has significant lowering effect on BP. We further sought to evaluate the performance of our proposed PLME model against the widely used JKME model, within the multivariate joint modeling framework through a simulation study mimicking a crossover design. From our findings, the multivariate joint PLME model performed exceptionally well both in estimation of random-effects matrix (G) and Hessian matrix (H), allowing satisfactory model convergence during estimation. It allowed a more complex fit to the data with both random intercepts and slopes effects compared to the multivariate joint JKME model that allowed for random intercepts only. When a hierarchical viewpoint is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive definite. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters. The key highlight in this evaluation shows that multivariate joint JKME model is a powerful tool especially while fitting mixed models with random intercepts only, in crossover design settings. Addition of random slopes may lead to model complexities in most cases, resulting in unsatisfactory model convergence during estimation. To circumvent convergence pitfalls, extention of JKME model to PLME model allows a more flexible fit to the data (generated from crossover design settings), especially in the multivariate joint modelingframework.

BackgroundNintedanib is used to treat both idiopathic and progressive pulmonary fibrosis (IPF/PPF). Evidence of both an exposure-response relationship and an exposure-toxicity relationship has been found, suggesting the potential value of therapeutic drug monitoring (TDM). We aimed to define the therapeutic window of nintedanib in a real-world cohort. MethodsData from two clinical studies were pooled for this analysis. To quantify exposure to nintedanib, a population-pharmacokinetic (PK) model was developed. Associations between PK and decline in forced vital capacity (FVC) and diffusing capacity (DLCO) were performed using linear-mixed-effect models (LMEM). The exposure-toxicity relationship was evaluated using a Cox proportional hazards model. ResultsIn total, 911 PK samples from 99 patients were used to develop the PK model. The LMEM with random slopes and intercepts included 517 pulmonary function tests (PFT) from 81 patients. The average administered nintedanib dose was associated with the rate of FVC decline (p=0.002). Per 50 mg decrease of daily dosage, the rate of FVC decline increased by 53.5 mL/year. Neither nintedanib exposure nor dose significantly affected DLCO decline and they were also not significantly associated with the occurrence of a dose-limiting toxicity (DLT). This may be explained by a large inter- and intrapatient variability in nintedanib PK. ConclusionNintedanib dose was significantly associated with FVC loss. However, no significant relationship between nintedanib exposure and the occurrence of DLTs was found in this real-world population, and no therapeutic window could be established. The findings in this study indicate that nintedanib is an unsuitable candidate for performing TDM.

This study characterizes the performance and success rate for fiberoptic-guided tracheal tube placement through the air-Q(®) Intubating Laryngeal Airway (air-Q). Using a manikin, anesthesia trainees and staff anesthesiologists, experienced in fiberoptic-guided intubation, performed five consecutive fiberoptic-guided tracheal tube placements via the air-Q. Participant characteristics, procedure segment times, total procedure times, and observed failures were recorded. Linear mixed effect models with random slopes and intercepts were used to assess participant performance. Ten anesthesia trainees and ten staff anesthesiologists participated. Anesthesia trainees were younger and had practiced for fewer years compared to staff anesthesiologists. Gender was equally distributed between the groups. Both segmental and overall procedure times decreased from the first to the fifth trial among all participants, independent of experience level and gender. Overall mean procedure time decreased from 102±31 to 68±14s, representing a relative time reduction of 33% and a mean time difference of 34s [95% confidence interval (CI) 22-47s; p<0.0001]. Tracheal tube placement was successful in all attempts; however, three tracheal tube dislodgements occurred during air-Q removal (overall procedure success 97%). Fiberoptic-guided tracheal tube placement through the air-Q can be performed in a clinically acceptable period of time with high success by operators skilled in fiberoptic-guided intubation. Tracheal tube dislodgement during air-Q removal remains a potential risk that should be emphasized.

Maternal and paternal physical abuse: Unique and joint associations with child behavioral problems