Advanced Statistical Methods Research Articles

CritSEM: Advancing QuantCrit to Examine Racialized Resegregation in Special Education

The field of education urgently needs research, aligned with QuantCrit, to disrupt the racialized overrepresentation [resegregation] of Black and Latinx students in special education. Our concern about how statistics have been (mis)applied motivated us to consider the possibility of quantitative methods for use in educational research that aims to dismantle white supremacy. Therefore, we developed CritSEM (Critical Structural Equation Modeling) as a methodological intervention for the purpose of telling a Critical Race Counterstory. Specifically, we examined the extent that anti-racist educators can disrupt processes causing the resegregation of Black and Latinx students in special education, using restricted data from the 2011 Early Childhood Longitudinal Study. Our study concludes with a discussion and implications about working toward an anti-racist special education system and applying QuantCrit in advanced statistical methods.

Assessment and predictive analysis of health professions students' proficiency in health economics and pharmacoeconomics in Southwest China: a cross-sectional study

ObjectivesThis study addresses the growing importance of health economics and pharmacoeconomics in preparing healthcare professionals for economic decision-making, particularly in China’s evolving healthcare system. Despite their significance, these subjects are inconsistently included in health education in Southwest China, making it crucial to assess students' proficiency and educational needs.MethodsA cross-sectional study was conducted from July to October 2023, involving medical and pharmaceutical programs across universities in Sichuan, Chongqing, Guizhou, and Yunnan. Data on students' knowledge, attitudes, and proficiency were captured using a structured questionnaire. Advanced statistical methods, including descriptive analysis, ANOVA, regression analysis, PCA, and Spearman correlation heatmaps, were employed for data analysis using IBM SPSS and Python. The sample consisted of 781 students, with the survey facilitated via the Questionnaire Star platform.ResultsThe survey revealed moderate comprehension of health economics and pharmacoeconomics among 781 health-related students, with an average knowledge score of 2.9. The majority were pharmacy students (45.58%) and aged 21–24 years (65.81%) with a significant female majority (64.92%). Academic year significantly influenced understanding, while gender differences were minimal. Spearman correlation indicated a strong linkage (0.78) between Supply and Demand and Cost Effectiveness, with minimal correlations between knowledge and attitudes towards the educational importance of health economics. Multiple regression analysis highlighted the impact of gender and academic progression on students' comprehension of Supply and Demand concepts, as well as the influence of knowledge levels on their attitudes towards the importance of health economics education. PCA indicated the complex structure of knowledge and attitudes.ConclusionsThe study found moderate knowledge levels in health economics among health professions students in Southwest China, with academic year affecting comprehension and minimal gender differences. Pharmacy students had higher proficiency in pharmaceutical-related areas, but gaps in complex topics like 'Economic Evaluation' highlight the need for targeted educational improvements to better prepare students for healthcare economic decision-making.

Experimental and Statistical Analysis of Iron Powder for Green Heat Production

In the current investigation, a novel methodology was employed to assess iron powder as a recyclable and sustainable energy carrier. Concurrently, an examination of the modeling of iron powder ignition and the ensuing heat output from the burner was undertaken. The flame temperature was determined by examining the light intensity emitted by the particles as they melted, which is directly related to the particle’s cross-sectional area. An account of the characterization of the experimental procedure, validation, and calibration is presented. Through measurements, distinct one-to-one correlations have been established between the scales of flame combustion and the temperatures of particles of varying sizes of iron. Additionally, a theoretical model for the combustion of expanding particles, particularly iron, within the diffusion-limited regime has been rigorously developed. This model delves into the spectra acquired from particle flames within the burner, utilizing Partial Least Squares Regression (PLSR) and Principal Component Analysis (PCA). This study investigates the use of optical fiber spectroscopy to predict flame temperature and assess iron powder size. The aim was to investigate how different sizes of iron powder affect flame temperature and to create calibration models for non-destructive prediction. The study shows that smaller particles had an average temperature of 1381 °C while larger particles reach up to 1842 °C, demonstrating the significant impact of particle size on combustion efficiency. The results were confirmed using advanced statistical methods, including PLSR and PCA, with PCA effectively differentiating between particle sizes and PLSR achieving an R2 value of 0.90 for the 30 µm particles.

Validating the Child Behavior Checklist 1.5-5 as a screening tool for autism spectrum disorder.

Early identification of Autism Spectrum Disorder is very important, especially in low and middle-income countries, where access to resources is often limited. The Child Behavior Checklist 1.5-5 is a tool that has been used to help identify children with autism spectrum disorder through specific behavior patterns. However, its effectiveness in low- and middle-income country settings has not been thoroughly studied. This research focused on evaluating the Child Behavior Checklist 1.5-5 as a screening tool for autism spectrum disorder among Brazilian children. The study involved 1292 children aged 3-5 years from the general population and 70 children with autism spectrum disorder aged 1-5 years. Using advanced statistical methods, the study tested how well the Child Behavior Checklist identified children with autism spectrum disorder and how reliable it was in this context. The findings showed that the Child Behavior Checklist 1.5-5 performed well in identifying autism spectrum disorder, with high reliability and consistency in the results. Although one item in each of the autism spectrum problems and withdrawn syndrome subscales did not perform as strongly, the overall tool was effective. In summary, the Child Behavior Checklist 1.5-5 proves to be a reliable and valid tool for early autism spectrum disorder screening in Brazilian children. This can help ensure that more children in low- and middle-income country settings are identified early and receive the necessary support and interventions to help them thrive. Future research should continue to test this tool in different contexts to confirm its usefulness across various populations.

Ensuring Quality and Consistency in OPC-53 Cement Testing: A Comprehensive Proficiency Testing Evaluation

The proficiency testing (PT) program is a critical part of quality assurance in laboratories, especially within industries like construction where cement testing is essential. This study provides a comprehensive evaluation of the fifth PT scheme for Ordinary Portland Cement (OPC) grade 53, conducted by the Nodal Laboratory. The key parameters assessed in this program were standard consistency, compressive strength, and soundness using the Le-Chatelier method. Twenty-four laboratories participated, and their performance was analyzed using advanced statistical methods, adhering to ISO 17043:2010 and ISO 13528:2015 standards. The results showed that most laboratories performed satisfactorily, with only one outlier identified in the soundness test. This paper covers all aspects of the PT program, from sample preparation to the distribution of materials, as well as the homogeneity and stability tests conducted on the samples. Statistical evaluation was carried out using Z-scores, which allowed the laboratories to be ranked according to their performance in each parameter. This method highlights discrepancies and provides a clear framework for participating laboratories to improve their testing procedures. Proficiency testing is essential for maintaining high standards in laboratory performance. It helps standardize testing methods across multiple laboratories, ensuring that OPC-53 cement meets strict quality and performance specifications. The program enables laboratories to identify performance gaps and take corrective actions, ensuring the reliability and accuracy of results. Participation in PT programs is a step toward continuous improvement, keeping laboratories aligned with international standards, ultimately benefiting the broader construction industry by ensuring the structural integrity of buildings and infrastructure. This study underscores the importance of regular PT participation. It not only provides a benchmark for laboratory performance but also ensures compliance with ISO standards. This fosters the consistent quality of cement used in large-scale construction, reducing the risk of structural failures and contributing to long-term safety and durability. The findings demonstrate the positive impact of PT programs on overall testing accuracy and laboratory reliability, emphasizing their role in advancing best practices in the construction industry.

Future directions in myelodysplastic syndromes/neoplasms and acute myeloid leukaemia classification: from blast counts to biology.

Myelodysplastic syndromes/neoplasms (MDS) and acute myeloid leukaemia (AML) are neoplastic haematopoietic cell proliferations that are diagnosed and classified based on a combination of morphological, clinical and genetic features. Specifically, the percentage of myeloblasts in the blood and bone marrow is a key feature that has historically separated MDS from AML and, together with several other morphological parameters, defines distinct disease entities within MDS. Both MDS and AML have recurrent genetic abnormalities that are increasingly influencing their definitions and subclassification. For example, in 2022, two new MDS entities were recognised based on the presence of SF3B1 mutation or bi-allelic TP53 abnormalities. Genomic information is more objective and reproducible than morphological analyses, which are subject to interobserver variability and arbitrary numeric cut-offs. Nevertheless, the integration of genomic data with traditional morphological features in myeloid neoplasm classification has proved challenging by virtue of its sheer complexity; gene expression and methylation profiling also can provide information regarding disease pathogenesis, adding to the complexity. New machine-learning technologies have the potential to effectively integrate multiple diagnostic modalities and improve on historical classification systems. Going forward, the application of machine learning and advanced statistical methods to large patient cohorts can refine future classifications by advancing unbiased and robust previously unrecognised disease subgroups. Future classifications will probably incorporate these newer technologies and higher-level analyses that emphasise genomic disease entities over traditional morphologically defined entities, thus promoting more accurate diagnosis and patient risk stratification.

Current State of Connected Sensor Technologies Used During Rehabilitation Care: Protocol for a Scoping Review.

Connected sensor technologies can capture raw data and analyze them using advanced statistical methods such as machine learning or artificial intelligence to generate interpretable behavioral or physiological outcomes. Previous research conducted on connected sensor technologies has focused on design, development, and validation. Published review studies have either summarized general technological solutions to address specific behaviors such as physical activity or focused on remote monitoring solutions in specific patient populations. This study aimed to map research that focused on using connected sensor technologies to augment rehabilitation services by informing care decisions. The Population, Concept, and Context framework will be used to define inclusion criteria. Relevant articles published between 2008 to the present will be included if (1) the study enrolled adults (population), (2) the intervention used at least one connected sensor technology and involved data transfer to a clinician so that the data could be used to inform the intervention (concept), and (3) the intervention was within the scope of rehabilitation (context). An initial search strategy will be built in Embase; peer reviewed; and then translated to Ovid MEDLINE ALL, Web of Science Core Collection, and CINAHL. Duplicates will be removed prior to screening articles for inclusion. Two independent reviewers will screen articles in 2 stages: title/abstract and full text. Discrepancies will be resolved through group discussion. Data from eligible articles relevant to population, concept, and context will be extracted. Descriptive statistics will be used to report findings, and relevant outcomes will include the type and frequency of connected sensor used and method of data sharing. Additional details will be narratively summarized and displayed in tables and figures. Key partners will review results to enhance interpretation and trustworthiness. We conducted initial searches to refine the search strategy in February 2024. The results of this scoping review are expected in October 2024. Results from the scoping review will identify critical areas of inquiry to advance the field of technology-augmented rehabilitation. Results will also support the development of a longitudinal model to support long-term health outcomes. Open Science Framework jys53; https://osf.io/jys53. DERR1-10.2196/60496.

Erosion of the temporal bone by vestibular schwannoma: morphometrics and predictive modeling.

To perform a comprehensive morphometric analysis of vestibular schwannomas (VS) using multimodal imaging, focusing on the relationship between tumor characteristics and internal acoustic canal (IAC) changes. We analyzed a cohort of patients undergoing radiosurgery for VS, utilizing high-definition MRI and bone CT for detailed anatomical assessment. Image co-registration and fusion techniques were employed to examine VS and IAC dimensions. Advanced statistical methods, including logistic regression, were applied to identify patterns of IAC dilation and establish predictive indicators for these morphological changes. The study included 659 patients (51.1% female, mean age 56.37years) with evenly distributed tumor lateralization. Koos grades were I (22.9%), II (29.9%), III (38.2%), IVa (8.9%), and IVb (0.3%). Most tumors (90.9%) extended both inside and outside the IAC. Ipsilateral IAC (IIAC) dimensions were significantly larger than contralateral, with IIAC volume 49% greater (p < .0001). Higher Koos grades correlated with increased intra-canalicular lesion volume (icLV), which was strongly associated with IIAC size. Logistic regression identified icLV as the strongest predictor of IIAC dilation (AUC = 0.7674, threshold = 137.52 mm3). The icLV appears central to the pathophysiological development of VS and its impact on IAC anatomy. While limited by a selective patient base and static imaging data, these findings enhance the understanding of VS-IAC interactions, offering insights for improved clinical management and further research.

Organizational learning programs to fulfilling basic needs at work: significance for work attractiveness and turnover in strained sectors

Purpose This study aims to explore whether participation in employer-provided skills and learning programs can strengthen older workers’ abilities to carry out their work in a meaningful way so that it increases work attractiveness and a willingness to remain in the current job position. Design/methodology/approach A survey was distributed to assistance nurses, nurses and teachers, aged 55 years and older in a Swedish City (n = 1,342), analyzed descriptively and with structural equation modeling. Findings This paper showed positive relationships between active participation in organizational learning programs (OLPs) and autonomy, relatedness, competence and also attractive work. Associations are observed between participation in learning programs, e.g. mentorship, through the strengthened basic needs at work with work attractiveness and lower intention to leave, but not prolonged retirement preference. Research limitations/implications The cross-sectional quantitative design restricts drawing causal conclusions about associations. Practical implications OLPs at work may be seen as potential measures to strengthen work conditions, fulfilling basic psychological needs at work and increase work attractiveness in strained welfare sectors. Social implications There are some welfare sectors that – more than others – are strained by challenges to maintain, sustain and develop quality, knowledge and staff due to poor economic and social resources with regard to sustainability, e.g. in the educational and caring sectors. Strengthening organizational measures is needed to support sustainable development. Originality/value This study applies advanced statistical methods, in a large empirical sample, and shows the importance of skills and learning programs for job attractiveness among older workers in female-dominated, strained welfare sectors.

Compare the teaching effectiveness and student satisfaction between native Japanese language teachers and non-native Japanese language teachers

This study examines the comparative teaching effectiveness and student satisfaction between native Japanese language teachers (NJLTs) and non-native Japanese language teachers (NNJLTs). Utilizing a sample of 740 students from various educational institutions in Japan, the research employs a quantitative design, including structured questionnaires adapted from established scales. Advanced statistical methods, including factor analysis and multiple regression, were used to analyze the data. The findings reveal no significant differences in student satisfaction and language proficiency between students taught by NJLTs and NNJLTs. Additionally, regression analysis showed that cultural relatability and empathy were not significant predictors of teaching effectiveness, suggesting that factors beyond nativeness influence student outcomes. These results challenge the native-speakerism ideology, highlighting the importance of pedagogical skills, teacher-student rapport, and effective teaching strategies. The study underscores the need for inclusive hiring practices, comprehensive teacher training programs, and collaborative teaching models that leverage the strengths of both NJLTs and NNJLTs. Implications for educational policy, curriculum design, and teacher professional development are discussed, advocating for a balanced approach that values the contributions of both native and non-native teachers. Limitations include the reliance on self-reported data and the specific cultural context of Japan. Future research should explore additional variables, employ longitudinal designs, and utilize mixed-methods approaches to provide a more nuanced understanding of language teaching effectiveness.

Customer Perception of Financial Performance of the State Bank of India and Its Associate Banks Before and After the Merger

This study explores customer perceptions of the financial performance of the State Bank of India (SBI) and its associate banks before and after their merger. The primary objective is to evaluate the financial performance of SBI and its associates, comparing pre- and post-merger scenarios. The study hypothesizes that customers perceive the performance of SBI post-merger to be significantly better than before the merger. A descriptive and analytical approach was employed, utilizing primary and secondary data. Primary data was collected through a structured questionnaire distributed among bank employees across various SBI branches in Karnataka. The data was analyzed using SPSS software, employing descriptive statistics such as mean, standard deviation, and percentage to assess the impact of the merger on SBI's performance. Advanced statistical methods were also applied to test the hypotheses, including Factor Analysis, Linear Regression, Pearson Correlation, Chi-Square Test, and One-way ANOVA. The results indicate significant improvements in customer perceptions of SBI's performance post-merger, affirming the hypothesis. This study provides a comprehensive analysis of the effects of the merger on SBI's financial performance, highlighting the importance of customer perception in evaluating the success of such corporate restructuring efforts.

Relationship between corporate governance and intellectual capital: Evidence from Jordan

The objective of this study is to examine the relationship between corporate governance and intellectual capital within Jordanian manufacturing firms. This study used a sample of Jordanian manufacturing firms and applied regression analysis to test the effects of board size, executive director duality, percentage of independent directors, and ownership concentration on intelligence capital performance. Thus, 64 Jordanian listed manufacturing firms represent the study sample for the study period (2014–2022). The study employs advanced statistical methods to evaluate how these governance mechanisms affect intellectual capital, including human, structural, and relational capital. The study results indicate that the board size and CEO duality had no significant impact on intellectual capital performance. A positive significant determinant is the firm performance measured by earnings per share with a coefficient estimate of 6.331 at p-value &amp;amp;lt;0.0. The significant positive effect of firm performance on intellectual capital performance indicates that financial health is an important driver of intellectual capital utilization. Good firms are likely to have more resources to invest in human capital, technology, and innovation, which are necessary components of intellectual capital. Future research should continue to explore these dynamics across different contexts to inform more effective governance and management practices.

The African Initiative for Bioinformatics Online Training in Neurodegenerative Diseases (AI‐BOND): Investing in the next generation of African neuroscientists

AbstractNeurodegenerative disorders, including Alzheimer's disease and AD‐related dementias (AD/ADRD), pose significant challenges to health care systems globally, particularly in Africa. With the advances in medical technology and research capabilities, especially in next‐generation sequencing and imaging, vast amounts of data have been generated from AD/ADRD research. Given that the greatest increase in AD/ADRD prevalence is expected to occur in Africa, it is critical to establish comprehensive bioinformatics training programs to help African scientists leverage existing data and collect additional information to untangle AD/ADRD heterogeneity in African populations. The South Texas Alzheimer's Disease Research Center, with efforts from the National Institutes of Health and the Global Brain Health Institute, has partnered with the Brain Research Africa Initiative to develop the African Initiative on Bioinformatics Online Training in Neurodegenerative Disease (AI‐BOND). AI‐BOND is a comprehensive and accessible training program, the aim of which is to advance biostatistics and bioinformatics expertise in Africa in studying neurodegenerative diseases. This expertise is essential to enable African scientists to utilize the extensive AD/ADRD data and enhance the continent's ability to contribute to global research efforts in this field. The training addresses the gap in analyzing neurodegenerative disease data by providing skills and knowledge in genetic epidemiology, biostatistics, and bioinformatics to African students and researchers. This innovative online training program will last 6 months and provide training in skill sets R, SAS, and Python programing, genome‐wide association studies, genomics, transcriptomics, proteomics, metabolomics, microbiome analysis, and advanced statistical methods. Additional training will include study design and manuscript and grant writing. The first cohort of the AI‐BOND program will graduate in June 2024. The AI‐BOND program is expected to build research computational capacities in Africa that will improve the ability of graduates to conduct and utilize large‐scale studies, with the goal of curbing the growing incidence of neurodegenerative diseases in Africa.Highlights Alzheimer's disease (AD) and AD‐related dementias (ADRD) pose significant health challenges globally, particularly in Africa. The most significant AD/ADRD prevalence increase is predicted to occur in Africa. It is crucial to establish a bioinformatics training capacity in Africa to leverage the vast number of multi‐omics and imaging biomarkers of AD/ADRD data being generated. The African Initiative on Bioinformatics Online Training in Neurodegenerative Disease (AI‐BOND) training addresses the gaps in study design, biostatistics, genetic epidemiology, and bioinformatics related to neurodegenerative diseases in Africa. The success of AI‐BOND is anticipated to help build computational research capacities in Africa.

Preprocessing of spectroscopic data to highlight spectral features of materials.

The study of the extensive data sets generated by spectrometers, which are of the type commonly referred to as big data, plays a crucial role in extracting valuable information on mineral composition in various fields, such as chemistry, geology, archaeology, pharmacy and anthropology. The analysis of these spectroscopic data falls into the category of big data, which requires the application of advanced statistical methods such as principal component analysis and cluster analysis. However, the large amount of data (big data) recorded by spectrometers makes it very difficult to obtain reliable results from raw data. The usual method is to carry out different mathematical transformations of the raw data. Here, we propose to use the affine transformation for highlight the underlying features for each sample. Finally, an application to spectroscopic data collected from minerals or rocks recorded by NASA's Jet Propulsion Laboratory is performed. An illustrative example has been included by analysing three mineral samples, which have different diageneses and parageneses and belong to different mineralogical groups.

Grid Integration Strategies for Optimizing Renewable Energy Deployment and Grid Resilience

This study explores the integration of renewable energy sources, namely, solar and wind, focusing on strategies to optimize their deployment into the electrical grid, and increasing the resiliency of the grid. Using four-year comprehensive data from Spain, including energy consumption, generation, pricing, and the condition of the weather, advanced statistical analysis, regression models, and optimization methods have been employed. Based on the results, it is clear that solar energy is seasonal, and wind energy is variable, with the weather playing a considerable role in the energy output. The optimization analysis showed that when the renewable capacity was increased to include 30 MW of solar and 120 MW of wind, the energy demand would be met at a significantly lower total system cost of $12.60 per unit. The costs related to operation and emissions would also decrease notably. However, with the regression models giving modest values of R² equal to 0.19 for solar and R² equal to 0.21 for wind, the extent of these developments and prediction can be fairly modest. Still, these results provide a strong backbone for the prediction of energy generation and show that modernization of the grid and adaptive management are of crucial importance. The results of the study could provide a guideline for policymakers and energy managers on how these goals can be achieved.

Comment on, "Creactive protein levels, the prognostic nutritional index, and the lactate dehydrogenasetolymphocyte ratio are important prognostic factors in primary central nervous system lymphoma: a singlecenter study of 223 patients".

This study by Zuo et al. (2024) investigates the prognostic significance of C-reactive protein (CRP) levels, the prognostic nutritional index (PNI), and the lactate dehydrogenase-to-lymphocyte ratio (LLR) in primary central nervous system lymphoma (PCNSL) using data from 223 patients. The research demonstrates that these markers are critical in predicting patient outcomes, offering novel insights beyond traditional prognostic models like the MSKCC and IELSG scores. Despite its strengths, the study's retrospective design and lack of validation cohort limit its generalizability. Future research should focus on validating these findings in diverse, multicenter settings and integrating these markers with existing prognostic models to improve clinical decision-making. Longitudinal studies and advanced statistical methods are recommended to further explore the interactions between these factors and their impact on patient outcomes, potentially leading to the development of targeted therapies for PCNSL.

Comprehensive statistical analysis reveals significant benefits of COVID-19 vaccination in hospitalized patients: propensity score, covariate adjustment, and feature importance by permutation

BackgroundCOVID-19 vaccines effectively prevent infection and hospitalization. However, few population-based studies have compared the clinical characteristics and outcomes of patients hospitalized for COVID-19 using advanced statistical methods. Our objective is to address this evidence gap by comparing vaccinated and unvaccinated patients hospitalized for COVID-19.MethodsThis retrospective cohort included adult COVID-19 patients admitted from March 2021 to August 2022 from 27 hospitals. Clinical characteristics, vaccination status, and outcomes were extracted from medical records. Vaccinated and unvaccinated patients were compared using propensity score analyses, calculated based on variables associated with vaccination status and/or outcomes, including waves. The vaccination effect was also assessed by covariate adjustment and feature importance by permutation.ResultsFrom the 3,188 patients, 1,963 (61.6%) were unvaccinated and 1,225 (38.4%) were fully vaccinated. Among these, 558 vaccinated individuals were matched with 558 unvaccinated ones. Vaccinated patients had lower rates of mortality (19.4% vs. 33.3%), invasive mechanical ventilation (IMV-18.3% vs. 34.6%), noninvasive mechanical ventilation (NIMV-10.6% vs. 22.0%), intensive care unit admission (ICU-32.0% vs. 44.1%) vasoactive drug use (21.1% vs. 32.6%), dialysis (8.2% vs. 14.7%) hospital length of stay (7.0 vs. 9.0 days), and thromboembolic events (3.9% vs.7.7%), p < 0.05 for all. Risk-adjusted multivariate analysis demonstrated a significant inverse association between vaccination and in-hospital mortality (adjusted odds ratio [aOR] = 0.42, 95% confidence interval [CI]: 0.31–0.56; p < 0.001) as well as IMV (aOR = 0.40, 95% CI: 0.30–0.53; p < 0.001). These results were consistent in all analyses, including feature importance by permutation.ConclusionVaccinated patients admitted to hospital with COVID-19 had significantly lower mortality and other severe outcomes than unvaccinated ones during the Delta and Omicron waves. These findings have important implications for public health strategies and support the critical importance of vaccination efforts, particularly in low-income countries, where vaccination coverage remains suboptimal.

Machine learning approaches to evaluate heterogeneous treatment effects in randomized controlled trials: a scoping review

Background and ObjectivesEstimating heterogeneous treatment effects (HTEs) in randomized controlled trials (RCTs) has received substantial attention recently. This has led to the development of several statistical and machine learning (ML) algorithms to assess HTEs through identifying individualized treatment effects. However, a comprehensive review of these algorithms is lacking. We thus aimed to catalog and outline currently available statistical and ML methods for identifying HTEs via effect modeling using clinical RCT data and summarize how they have been applied in practice. Study Design and SettingWe performed a scoping review using prespecified search terms in MEDLINE and Embase, aiming to identify studies that assessed HTEs using advanced statistical and ML methods in RCT data published from 2010 to 2022. ResultsAmong a total of 32 studies identified in the review, 17 studies applied existing algorithms to RCT data, and 15 extended existing algorithms or proposed new algorithms. Applied algorithms included penalized regression, causal forest, Bayesian causal forest, and other metalearner frameworks. Of these methods, causal forest was the most frequently used (7 studies) followed by Bayesian causal forest (4 studies). Most applications were in cardiology (6 studies), followed by psychiatry (4 studies). We provide example R codes in simulated data to illustrate how to implement these algorithms. ConclusionThis review identified and outlined various algorithms currently used to identify HTEs and individualized treatment effects in RCT data. Given the increasing availability of new algorithms, analysts should carefully select them after examining model performance and considering how the models will be used in practice.

Analysis of Indirect Costs of Absence Associated with Mental Disorders on the Basis of Social Security Data (2012-2023).

Mental and behavioral disorders significantly impair psychophysical functioning, leading to challenges in daily activities. The increasing recognition of the importance of mental health in global development goals has resulted in its inclusion in the United Nations' Sustainable Development Goals. The burden of mental disorders has grown worldwide due to demographic changes, with substantial economic and social impacts. This study aimed to examine the indirect costs of mental disorders in Poland by analyzing the expenditures by the Social Insurance Institution (ZUS) on work incapacity benefits and disability pensions from 2012 to 2023. The goal was to identify trends, dependencies, and the economic impact of policy changes. Data were collected from ZUS reports on annual expenditures for work incapacity benefits and disability pensions. Advanced statistical methods, including linear regression and Pearson correlation, were employed to analyze trends and relationships. Student's t-tests assessed the statistical significance of the observed trends. The expenditures on benefits for work incapacity due to mental disorders increased significantly over the past decade, particularly from 2020 to 2023, partly due to the COVID-19 pandemic. Short-term absenteeism due to mental disorders accounted for 10.5% of the total sick leave days. A strong positive correlation was found between years and benefit expenditures. Conversely, the disability pension expenditures exhibited a downward trend, potentially reflecting improvements in public health or policy changes. The significant increase in expenditures on benefits related to mental disorders may reflect greater awareness, improved diagnostics, and the impact of the COVID-19 pandemic. In contrast, the decline in disability pension expenditures could suggest improved mental health or effective policy measures. However, it is important to emphasize that the presented data are not the only factor influencing this situation. Multiple variables, including societal, economic, and healthcare system changes, contribute to these trends. Therefore, further research is necessary to fully understand the underlying causes and to guide effective policy development. Regular monitoring and continued investment in mental health are essential to managing indirect costs such as absenteeism and presenteeism efficiently.

Understanding Latent Variables in EFL Contexts

Understanding latent variables is essential in EFL research. This article examines key latent variables, such as linguistic competence, cognitive ability and socio-cultural factors. These variables play a crucial role in shaping EFL learning experiences and outcomes. Researchers can use methods such as exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM) to uncover these variables. By combining theoretical findings with advanced statistical methods, such research provides an understanding of the complex processes involved in language learning. The article points to the need for longitudinal studies and mixed methods to capture the dynamic nature of EFL learning. This approach could contribute to the development of more effective educational strategies that improve teaching practices and learner outcomes. The article also emphasizes the importance of considering the sociocultural context and developing culturally sensitive teaching methods to create an inclusive learning environment. Moreover, this article serves as a guide for future research and practice in language learning.