Random Slope Research Articles

Clinical implications of nintedanib pharmacokinetics in patients with pulmonary fibrosis

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.

IoT-Driven Healthcare an Ensemble Learning Approach for Early Detection and Prevention of Chronic Diseases

This investigate presents an IoT-driven healthcare system leveraging outfit learning methods for early discovery and avoidance of unremitting illnesses. By joining information from wearable sensors, shrewd domestic gadgets, and electronic wellbeing records, the system offers a comprehensive wellbeing checking framework. Gathering learning calculations, counting Random Forest, Slope Boosting, Stacking, and Bolster Vector Machines, are utilized to combine numerous models' qualities, improving forecast exactness and robustness. Experimental comes about on an assorted dataset illustrate the viability of the proposed approach, accomplishing a precision of 85%, accuracy of 87%, review of 84%, F1-score of 85%, and AUC-ROC of 0.92. Comparative examinations with standard calculations and related work within the field emphasize the prevalence of the outfit learning approach in leveraging IoT information for healthcare applications. This investigation contributes to progressing IoT-driven healthcare by giving bits of knowledge into the potential of gathering learning procedures for illness expectation and avoidance. By making strides early location and personalized mediations, the system points to upgraded understanding results, diminishing healthcare costs, and progress in populace well-being management.

Point-of-Care-Ultrasound Quality Assurance Data from Fellow-Performed Exams in a Pediatric Emergency Department: A Descriptive Study.

Point-of-care-ultrasound (POCUS) is increasingly used by pediatric emergency medicine (PEM) fellows, but scant data exists on the accuracy of exam interpretations. Our goal was to determine whether agreement on exam interpretation between quality assurance (QA) faculty (reference standard) and PEM fellows varied by fellowship year or exam type. Retrospective review of fellow-performed POCUS exams between January 2019 and June 2022. Negative binomial (NB) random effects regression was used to account for longitudinal measurement of individual fellow performance across 3 years. Fixed effects were exam type and fellowship year. To assess between- and within-user variability across time, a random intercept and slope were included for each fellow. Exactly 3032 exams, performed by 24 fellows, were included. Raw proportion agreement by fellowship year was high for all exam types (≥88%). From the NB model, there was no statistically significant effect of fellowship year on the mean count of agreement. The relative risk (RR) of agreement for exam types was greatest for cardiac vs other types. The standard deviations for the random intercept and random slope were 0.09 and 0.04, respectively, with a correlation of -0.94. PEM fellows generally interpret exams correctly, with little variation through fellowship, although those who began with more basic skills showed more progress over time. Fellowship year did not influence the likelihood of correct interpretation but there was variation across exam type, with the best agreement for cardiac exams. The extent to which disagreements between fellows and QA faculty represent clinically significant errors requires further study.

Utilizing Generalized Linear Mixed Effects Models for Prediction of Bone Mineral Decrease After Lung Transplant - A Preliminary Study.

Recipients of lung transplants are at heightened risk of developing low bone mineral density (BMD). They also have increased risk of developing osteoporosis-related fractures. This rate of BMD decline is not well characterized. The aim of this manuscript is to characterize the rate of BMD decrease after lung transplant. This is a preliminary retrospective cohort study of 200 patients who received lung transplants. Each patient had pre-transplant dual energy X-ray absorptiometry (DXA) and post-transplant DXA scans on the same DXA scanner. The BMD at each lumbar vertebra, total hip, and femoral neck were recorded. Generalized linear mixed effects models with random intercepts and random slopes were used to model the rate of change of BMD after transplant, adjusting for time since transplant, sex, and sex*time interaction. We found that men had higher baseline BMD than women at all sites (P<0.05 for all). The rate of BMD decrease was fastest at the femoral neck (P<0.05). Men lost BMD at a faster rate (-5.23 x10-5 g/cm2/day) than women at the femoral neck (-2.22 x10-5 g/cm2 /day). BMD decreases after lung transplant and occurs faster in men than women.

Classroom social hierarchy and associations between children's behavioral control and peer relationships

Our study investigated concurrent associations between classroom-level social hierarchy and preschool to third-grade children's friendship and victimization. We considered its moderating role in the associations between children's behavioral control and their friendship and victimization, and explored grade level differences. We conducted three parallel sets of analyses using three operationalizations of classroom social hierarchy: social impact, peer liking, and social preference. Results revealed the three operationalizations of classroom social hierarchy were positively associated with victimization but not friendship. In hierarchical classrooms (operationalized as social preference), children with higher behavioral control had more friends and experienced less victimization (though random slope was nonsignificant) than children with lower behavioral control. In less hierarchical classrooms, behavioral control was unrelated to friendship and victimization. Analyses using social impact or peer liking did not show significant interactions. Associations were consistent across grade levels. Findings underscore the roles of classroom social hierarchy in young children's peer relationships.

Decomposing Impact on Longitudinal Outcome of Time-Varying Covariate into Baseline Effect and Temporal Effect

Longitudinal processes are often associated with each other over time; therefore, it is important to investigate the associations among developmental processes and understand their joint development. The traditional latent growth curve model (LGCM) with a time-varying covariate (TVC) provides a method to estimate the TVC effect on a longitudinal outcome while modeling the outcome’s change. However, it does not allow the TVC to predict variations in the random growth coefficients. We propose decomposing the TVC into initial trait and temporal states using three methods to address this limitation. In each method, the baseline of the TVC is viewed as an initial trait, and the corresponding effects are obtained by regressing random intercepts and slopes on the baseline value. Temporal states are characterized as (a) interval-specific slopes, (b) interval-specific changes, or (c) changes from the baseline at each measurement occasion, depending on the method. We demonstrate our methods through simulations and real-world data analyses, assuming a linear–linear functional form for the longitudinal outcome. The results demonstrate that LGCMs with a decomposed TVC can provide unbiased and precise estimates with target confidence intervals. We also provide OpenMx and Mplus 8 code for these methods with commonly used linear and nonlinear functions.

Regional Variability in MRI Scans with Different Magnetic Field Strengths in Japan: Implications for Healthcare Preparedness for Alzheimer's Disease Treatment.

(1) Background: The 2023 approval of lecanemab for early-stage Alzheimer's disease (AD) highlighted the need for routine 1.5T or 3.0T MRI scans to monitor amyloid-related imaging abnormalities (ARIAs). Regional disparities in MRI scan frequency, MRI scanner availability, and scanner magnetic field strengths could affect readiness for anti-amyloid therapy and lead to inconsistencies in ARIA detection nationwide. (2) Methods: We assessed regional variance in MRI scan frequency and field strength across Japan using the National Database (NDB) Open Data website, which summarizes Japanese public health insurance claims from the fiscal years (FYs) 2015 to 2021. We employed a mixed-effects model with prefecture-level random intercepts and slopes over time, subsequently categorizing prefectures into clusters based on MRI usage. (3) Results: 1.5T MRI was the most common magnetic field strength, remaining stable from FY2015 to FY2021. 3.0T MRI usage slightly increased, although the COVID-19 pandemic in FY2020 led to a maximum reduction of 5%. Prefecture-level variance was higher for 3.0T MRIs, with more frequent usage in western Japan. (4) Conclusions: This study highlights prefecture-level variance in MRI usage across Japan. The insights gained could be instrumental in improving healthcare preparedness for anti-amyloid treatment and patient management.

A novel block-coordinate gradient descent algorithm for simultaneous grouped selection of fixed and random effects in joint modeling.

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.

The effectiveness of coronary computed tomography angiography and functional testing for the diagnosis of obstructive coronary artery disease: results from the individual patient data Collaborative Meta-Analysis of Cardiac CT (COME-CCT)

AimTo determine the effectiveness of functional stress testing and computed tomography angiography (CTA) for diagnosis of obstructive coronary artery disease (CAD).Methods and resultsTwo-thousand nine-hundred twenty symptomatic stable chest pain patients were included in the international Collaborative Meta-Analysis of Cardiac CT consortium to compare CTA with exercise electrocardiography (exercise-ECG) and single-photon emission computed tomography (SPECT) for diagnosis of CAD defined as ≥ 50% diameter stenosis by invasive coronary angiography (ICA) as reference standard. Generalised linear mixed models were used for calculating the diagnostic accuracy of each diagnostic test including non-diagnostic results as dependent variables in a logistic regression model with random intercepts and slopes. Covariates were the reference standard ICA, the type of diagnostic method, and their interactions. CTA showed significantly better diagnostic performance (p < 0.0001) with a sensitivity of 94.6% (95% CI 92.7–96) and a specificity of 76.3% (72.2–80) compared to exercise-ECG with 54.9% (47.9–61.7) and 60.9% (53.4–66.3), SPECT with 72.9% (65–79.6) and 44.9% (36.8–53.4), respectively. The positive predictive value of CTA was ≥ 50% in patients with a clinical pretest probability of 10% or more while this was the case for ECG and SPECT at pretest probabilities of ≥ 40 and 28%. CTA reliably excluded obstructive CAD with a post-test probability of below 15% in patients with a pretest probability of up to 74%.ConclusionIn patients with stable chest pain, CTA is more effective than functional testing for the diagnosis as well as for reliable exclusion of obstructive CAD. CTA should become widely adopted in patients with intermediate pretest probability.Systematic review registrationPROSPERO Database for Systematic Reviews—CRD42012002780.Critical relevance statementIn symptomatic stable chest pain patients, coronary CTA is more effective than functional testing for diagnosis and reliable exclusion of obstructive CAD in intermediate pretest probability of CAD.Key PointsCoronary computed tomography angiography showed significantly better diagnostic performance (p < 0.0001) for diagnosis of coronary artery disease compared to exercise-ECG and SPECT.The positive predictive value of coronary computed tomography angiography was ≥ 50% in patients with a clinical pretest probability of at least 10%, for ECG ≥ 40%, and for SPECT 28%.Coronary computed tomography angiography reliably excluded obstructive coronary artery disease with a post-test probability of below 15% in patients with a pretest probability of up to 74%.Graphical

Associations of Amyloid Burden, White Matter Hyperintensities, and Hippocampal Volume With Cognitive Trajectories in the 90+ Study.

Amyloid pathology, vascular disease pathology, and pathologies affecting the medial temporal lobe are associated with cognitive trajectories in older adults. However, only limited evidence exists on how these pathologies influence cognition in the oldest old. We evaluated whether amyloid burden, white matter hyperintensity (WMH) volume, and hippocampal volume (HV) are associated with cognitive level and decline in the oldest old. This was a longitudinal, observational community-based cohort study. We included participants with 18F-florbetapir PET and MRI data from the 90+ Study. Amyloid load was measured using the standardized uptake value ratio in the precuneus/posterior cingulate with eroded white matter mask as reference. WMH volume was log-transformed. All imaging measures were standardized using sample means and SDs. HV and log-WMH volume were normalized by total intracranial volume using the residual approach. Global cognitive performance was measured by the Mini-Mental State Examination (MMSE) and modified MMSE (3MS) tests, repeated every 6 months. We used linear mixed-effects models with random intercepts; random slopes; and interaction between time, time squared, and imaging variables to estimate the associations of imaging variables with cognitive level and cognitive decline. Models were adjusted for demographics, APOE genotype, and health behaviors. The sample included 192 participants. The mean age was 92.9 years, 125 (65.1%) were female, 71 (37.0%) achieved a degree beyond college, and the median follow-up time was 3.0 years. A higher amyloid load was associated with a lower cognitive level (βMMSE = -0.82, 95% CI -1.17 to -0.46; β3MS = -2.77, 95% CI -3.69 to -1.84). A 1-SD decrease in HV was associated with a 0.70-point decrease in the MMSE score (95% CI -1.14 to -0.27) and a 2.27-point decrease in the 3MS score (95% CI -3.40 to -1.14). Clear nonlinear cognitive trajectories were detected. A higher amyloid burden and smaller HV were associated with faster cognitive decline. WMH volume was not significantly associated with cognitive level or decline. Amyloid burden and hippocampal atrophy are associated with both cognitive level and cognitive decline in the oldest old. Our findings shed light on how different pathologies contributed to driving cognitive function in the oldest old.

Post-COVID-19 recovery and geriatric rehabilitation care: a European inter-country comparative study.

There is variation in organization of geriatric rehabilitation across Europe. The purpose of this study was to describe the selection criteria for referral to geriatric rehabilitation, care provided, and recovery trajectories of post-COVID-19 patients referred to geriatric rehabilitation in Europe. This observational cohort study included 723 patients in 59 care facilities for geriatric rehabilitation across 10 countries. Patient data were collected from medical records on admission to geriatric rehabilitation (between September 2020 and October 2021), discharge, 6weeks and 6months follow-up. The primary and secondary outcomes were recovery in daily functioning (Barthel Index) and Quality of Life (EQ-5D-5L) from admission to discharge. These were examined using linear mixed models with two levels (measurements nested in patients) and country as an independent variable. Random intercept and random linear slope parameters were added when they improved model fit. A survey about organization of geriatric rehabilitation for post-COVID-19 patients was filled out by country coordinators and data were analyzed using descriptive statistics and inductive coding of answers to open questions. Patients had a mean age of 75.7years old and 52.4% were male. Many countries used various combinations of the selection criteria, such as functional status, age, frailty, Comprehensive Geriatric Assessment, comorbidities, and cognitive impairments. Most patients received physiotherapy (88.8%) and occupational therapy (69.7%), but there was substantial variance between countries in the percentages of patients that received protein or calorie enriched diets, oxygen therapy, and other treatment components. In all countries, patients showed recovery in daily functioning and quality of life, although there was variation in between countries in rate of recovery. Daily functioning seemed to increase most rapidly in the Czech Republic, Germany, and Russia. The steepest increases in quality of life were seen in the Czech Republic, Germany, and Spain. Post-COVID-19 patients showed recovery during geriatric rehabilitation, albeit at variable rates. The observed variation may be explained by the heterogeneity in selection criteria and care provided. This study highlights the need for harmonization of measurements in geriatric rehabilitation order to perform explanatory research and optimize geriatric rehabilitation throughout Europe to ensure optimal patient recovery.

Individual capacity growth over time in leadership courses: an intra-individual multilevel model approach

PurposeOur study was designed to investigate the longitudinal trajectories of student leader development capacities in a sample of students enrolled in multiple leadership-focused courses across several semesters. Our goal was to assess the degree to which course enrollment was associated with growth over the time that students engage as undergraduates in academic leadership programs, and if so, to assess the shape and speed of capacity change.Design/methodology/approachWe utilized a multilevel intra-individual modeling approach assessing students’ motivation to lead, leader self-efficacy, and leadership skills across multiple data collection points for students in a campus major or minor focused on leadership studies. We compared an unconditional model, a fixed effect model, a random intercept model, a random slope model, and a random slope and intercept model to determine the shape of score trajectories. Our approach was not to collect traditional pre-test and post-test data – choosing to collect data only at the beginning of each semester – to reduce time cues typically inherent within pre-test and post-test collections.FindingsOur results strongly suggested that individual students differ greatly in the degree to which they report the capacity to lead when initially enrolling in their first class. Surprisingly, the various models were unable to predict a pattern of longitudinal leader development through repeated course enrollment in our sample.Originality/valueOur investigation employed statistical methods that are not often utilized in leadership education quantitative research, and also included a data collection effort designed to avoid a linear pre-test/post-test score comparison.

Estimating nonlinear effects of random slopes: A comparison of multilevel structural equation modeling with a two-step, a single-indicator, and a plausible values approach

Multilevel structural equation modeling (MSEM) is a statistical framework of major relevance for research concerned with people’s intrapersonal dynamics. An application domain that is rapidly gaining relevance is the study of individual differences in the within-person association (WPA) of variables that fluctuate over time. For instance, an individual’s social reactivity – their emotional response to social situations – can be represented as the association between repeated measurements of the individual’s social interaction quantity and momentary well-being. MSEM allows researchers to investigate the associations between WPAs and person-level outcome variables (e.g., life satisfaction) by specifying the WPAs as random slopes in the structural equation on level 1 and using the latent representations of the slopes to predict outcomes on level 2. Here, we are concerned with the case in which a researcher is interested in nonlinear effects of WPAs on person-level outcomes – a U-shaped effect of a WPA, a moderation effect of two WPAs, or an effect of congruence between two WPAs – such that the corresponding MSEM includes latent interactions between random slopes. We evaluate the nonlinear MSEM approach for the three classes of nonlinear effects (U-shaped, moderation, congruence) and compare it with three simpler approaches: a simple two-step approach, a single-indicator approach, and a plausible values approach. We use a simulation study to compare the approaches on accuracy of parameter estimates and inference. We derive recommendations for practice and provide code templates and an illustrative example to help researchers implement the approaches.

Characterising the killing of girls and women in urban settings in Latin America, 2000–2019: an analysis of variability and time trends using mortality data from vital registration systems

IntroductionLatin America is burdened by high levels of violence. Although boys and men often experience more violence and fatalities, girls and women face a greater risk of being killed by...

A novel approach to stability analysis of random soil-rock mixture slopes using finite element method in ABAQUS

A novel approach to stability analysis of random soil-rock mixture slopes using finite element method in ABAQUS

Optimizing short-term in-class process evaluation: analyzing the effectiveness of teaching interventions in pharmaceutical education using repeated measures analysis

BackgroundThe assessment of the effectiveness of teaching interventions in enhancing students’ understanding of the Pharmaceutical Care Network Europe (PCNE) Classification System is crucial in pharmaceutical education. This is especially true in regions like China, where the integration of the PCNE system into undergraduate teaching is limited, despite its recognized benefits in addressing drug-related problems in clinical pharmacy practice. Therefore, this study aimed to evaluate the effectiveness of teaching interventions in improving students’ understanding of the PCNE Classification System in pharmaceutical education.MethodsUndergraduate pharmacy students participated in a series of sessions focused on the PCNE system, including lectures (t1), case analyses (t2), and practical implementation (t3). The levels of understanding were evaluated using time-course questionnaires. Initially, paired samples t-Tests were used to compare understanding levels between different time points. Subsequently, Repeated Measures Analysis (RMA) was employed. Pearson correlation analysis was conducted to examine the relationship between understanding levels and the usability and likelihood of using the PCNE system, as reported in the questionnaires.ResultsThe paired samples t-Tests indicated insignificant differences between t2 and t3, suggesting limited improvement following the practical implementation of the PCNE system. However, RMA revealed significant time effects on understanding levels in effective respondents and the focused subgroup without prior experience (random intercept models: all p < 0.001; random slope models: all p < 0.001). These results confirmed the effectiveness of all three teaching interventions. Pearson correlation analysis demonstrated significant positive correlations between understanding levels and the usability and likelihood of using the PCNE system at all examined time points. This finding highlighted the reliability of the understanding levels reported in the questionnaires. The homework scores were used as external calibration standards, providing robust external validation of the questionnaire’s validity.ConclusionThe implementation of RMA provided robust evidence of the positive impact of time on understanding levels. This affirmed the effectiveness of all teaching interventions in enhancing students’ comprehension of the PCNE Classification System. By utilizing RMA, potential errors inherent in common statistical methods, such as t-Tests, were mitigated. This ensured a more comprehensive and accurate assessment of the effectiveness of the teaching interventions.

Clarifying the relationship between trait aggression and self-control using random item slope regression.

Myriad psychological research evinces a negative association between self-control and aggression with some arguing for self-control failure as a cause of aggression. Recent literature suggests that the relationship between aggression and self-control is likely more complex and even positive in some cases. One source of such conflict in the literature could be the presence of unaccounted for random item slopes in commonly used measures of self-control which may inflate the likelihood of Type I errors. This study (N = 1386) tested the hypothesis that self-control would share random item slopes with the facets of trait aggression using random item slope regression. We measured trait aggression and self-control via two common self-reports: the Buss-Perry Questionnaire and the Brief Self-Control Scale. Our analyses revealed that the facets of trait aggression shared significant random item slopes with self-control and that many of these slopes were positive, rather than negative. We also found that Type I error inflation was evident in models that did not account for these random slopes. These findings may in part explain some of the conflicting results in the literature and that researchers interested in studying self-control and aggression should test for random item slopes.

Beyond Personal Factor: A Multilevel Analysis for Predicting the Effect of Moral Self-regulation and School Climate on Muslim Adolescent Aggressivity

Aggressive behaviors among adolescents have been increasingly observed in Indonesian Islamic boarding schools (pesantren), garnering significant attention due to their incongruity with the schools' ethos. Addressing these behaviors in pesantren requires a different approach from mainstream schools due to their unique characteristics. This study investigates the impact of moral self-regulation and school climate on aggressiveness among Muslim adolescents, using a multilevel analysis that considers both individual and institutional factors within the educational setting as units of analysis. This quantitative study involved 428 participants (mean=16.45, SD=1.01) both males and females from ten pesantren in three provinces in Indonesia selected using convenience sampling. Data were collected using the modified version of the aggressivity scale, moral self-regulation scale, and school climate scale. They were analyzed using multilevel regression analysis techniques with the aid of the R lmerTest program. An initial evaluation of the need for multilevel analysis was done by checking the significance of the Likelihood Ratio Test (LRT) results for the Intercept models and Intraclass correlation (ICC) above 5%. LRT and Akaike Information Criterion (AIC) were used as the selection criteria for the best hierarchical models involving random intercepts and random slopes. The study found that better moral self-regulation in students significantly reduces adolescent aggressivity. Additionally, a positive school climate helps prevent aggressive behavior among students. Addressing aggression in Muslim adolescents in Indonesia requires focusing on both individual and school factors that promote positive behavior, emphasizing the importance of moral self-regulation and a supportive school environment.

Implementing linear mixed effects models to enhance estimation of the dimensional stability of wood of Laurus nobilis L.

Aim of study: The properties of wood of laurel (Laurus nobilis L.) have not yet been adequately described. For example, information on variables related to dimensional stability during drying (shrinkage) is lacking, even though this is a key factor determining the suitability of the material for industrial uses with high added value. The aim of this study was to construct models for estimating shrinkage variables by using wood density as the predictor variable. Area of study: Seventeen laurel trees were felled in an inland area of Galicia (north-western Spain) in order to obtain the material for testing and modelling. Material and methods: The experimental tests were performed on 958 small standardised, defect-free wood specimens. Main results: The wood under study was moderately heavy and volumetrically unstable. Density varied only slightly, but volumetric shrinkage varied statistically significantly within and between trees. A linear mixed effects model was constructed to predict the variation in volumetric shrinkage from the oven-dry density, including the factors tree and height in the stem, with random slopes and intercepts. Research highlights: The model proved valid for all sampled individuals up to a height of two metres in the stem, thus enabling estimation of the volumetric shrinkage in commercial basal logs.

Predicting individual thermal preferences in an office: Assessing the performance of mixed-effects models

Multiple studies suggested that existing thermal comfort models inadequately predict occupants' individual thermal preferences. Personalised comfort models offer an alternative to conventional comfort models aiming to forecast individual's thermal preference. Implementation of these personalised models in occupant-centric control of heating, ventilation, and air-conditioning (HVAC) systems can enhance their performance.A promising technique for personalised comfort modelling is mixed-effects (ME) modelling. A ME model accounts for fixed effects, representing the trends in the general sample, and for random effects, representing variations of specific clusters in the data. In contrast to fixed-effects (FE) models, ME models can capture individual differences. However, its effectiveness in predicting occupants' thermal preferences based on field measurement data, as well as the influence of variations in ME models on prediction accuracy, remains to be thoroughly investigated.This study aims to assess the prediction accuracy of ME models in contrast to FE models using field measurement data, including thermal preference votes from 30 unique occupants. The prediction performance was evaluated across three testing scenarios, each representing a different application of the models. Furthermore, two random effect structures were tested for the ME model: an intercept-only model and an intercept and slope model. The results show that ME models, in comparison to FE models, achieve an improved prediction performance of 8.0 % on average and up to 28.4 % for individual occupants. Moreover, the addition of a random slope to the ME resulted in deteriorated predictions. Finally, occupants’ individual variations were determined with an uncertainty of 6 % after 20 observations.