An overview of statistical methods for biomarkers relevant to early clinical development of cancer immunotherapies.

Over the last decade, a new paradigm for cancer therapies has emerged which leverages the immune system to act against the tumor. The novel mechanism of action of these immunotherapies has also introduced new challenges to drug development. Biomarkers play a key role in several areas of early clinical development of immunotherapies including the demonstration of mechanism of action, dose finding and dose optimization, mitigation and prevention of adverse reactions, and patient enrichment and indication prioritization. We discuss statistical principles and methods for establishing the prognostic, predictive aspect of a (set of) biomarker and for linking the change in biomarkers to clinical efficacy in the context of early development studies. The methods discussed are meant to avoid bias and produce robust and reproducible conclusions. This review is targeted to drug developers and data scientists interested in the strategic usage and analysis of biomarkers in the context of immunotherapies.

Addressing immortal time bias in precision medicine: Practical guidance and methods development.

OBJECTIVE: To compare theoretical strengths and limitations of common immortal time adjustment methods, propose a new approach using multiple imputation (MI), and provide practical guidance for using MI in precision medicine evaluations centered on a real-world case study. STUDY SETTING AND DESIGN: Methods comparison, guidance, and real-world case study based on previous literature. We compared landmark analysis, time-distribution matching, time-dependent analysis, and our proposed MI application. Guidance for MI spanned (1) selecting the imputation method; (2) specifying and applying the imputation model; and (3) conducting comparative analysis and pooling estimates. Our case study used a matched cohort design to evaluate overall survival benefits of whole-genome and transcriptome analysis, a precision medicine technology, compared to usual care for advanced cancers, and applied both time-distribution matching and MI. Bootstrap simulation characterized imputation sensitivity to varying data missingness and sample sizes. DATA SOURCES AND ANALYTIC SAMPLE: Case study used population-based administrative data and single-arm precision medicine program data from British Columbia, Canada for the study period 2012 to 2015. PRINCIPAL FINDINGS: While each method described can reduce immortal time bias, MI offers theoretical advantages. Compared to alternative approaches, MI minimizes information loss and better characterizes statistical uncertainty about the true length of the immortal time period, avoiding false precision. Additionally, MI explicitly considers the impacts of patient characteristics on immortal time distributions, with inclusion criteria and follow-up period definitions that do not inadvertently risk biasing evaluations. In the real-world case study, survival analysis results did not substantively differ across MI and time distribution matching, but standard errors based on MI were higher for all point estimates. Mean imputed immortal time was stable across simulations. CONCLUSIONS: Precision medicine evaluations must employ immortal time adjustment methods for unbiased, decision-grade real-world evidence generation. MI is a promising solution to the challenge of immortal time bias.

A comparative study of home healthcare quality in urban and rural home health agencies throughout the USA (2010-22).

Urban-rural disparities in medical care, including in home healthcare, persist globally. With aging populations and medical advancements, demand for home health services rises, warranting investigation into home healthcare disparities. Our study aimed to (i) investigate the impact of rurality on home healthcare quality, and (ii) assess the temporal disparities and the changes in disparities in home healthcare quality between urban and rural home health agencies (HHAs), incorporating an analysis of geospatial distribution to visualize the underlying patterns. This study analyzed data from HHAs listed on the Centers for Medicare and Medicaid Services website, covering the period from 2010 to 2022. Data were classified into urban and rural categories for each HHA. We employed panel data analysis to examine the impact of rurality on home healthcare quality, specifically focusing on hospital admission and emergency room (ER) visit rates. Disparities between urban and rural HHAs were assessed using the Wilcoxon test, with results visualized through line and dot plots and heat maps to illustrate trends and differences comprehensively. Rurality is demonstrated as the most significant variable in hospital admission and ER visit rates in the panel data analysis. Urban HHAs consistently exhibit significantly lower hospital admission rates and ER visit rates compared to rural HHAs from 2010 to 2022. Longitudinally, the gap in hospital admission rates between urban and rural HHAs is shrinking, while there is an increasing gap in ER visit rates. In 2022, HHAs in Mountain areas, which are characterized by a higher proportion of rural regions, exhibited higher hospital admission and ER visit rates than other areas. This study underscores the persistent urban-rural disparities in home healthcare quality. The analysis emphasizes the ongoing need for targeted interventions to address disparities in home healthcare delivery and ensure equitable access to quality care across urban and rural regions. Our findings have the potential to inform policy and practice, promoting equity and efficiency in the long-term care system, for better health outcomes throughout the USA.

Identification of Amino Acids and Polyphenolic Metabolites in Human Plasma by UHPLC-ESI-QTOF-MS/MS, after the Chronic Intake of a Functional Meal in an Elderly Population.

Novel foods especially formulated and targeted for the elderly population should provide sufficient nutrients and bioactive ingredients to counteract the natural age-related deterioration of various organs and tissues. Dietary protein and phenolic compounds achieve this goal; however, older adults have alterations in their gastrointestinal system that may impact their bioavailability and few studies have been aimed at this population. Since phenolic compounds are the subject of multiple biotransformations by host and microbiome enzymes during the digestion process, identification of their bioavailable forms in human plasma or tissues represents a considerable analytical challenge. In this study, UHPLC-ESI-QTOF/MS-MS, chemometrics, and multivariate statistical methods were used to identify the amino acids and phenolic compounds that were increased in the plasma of elderly adults after a 30-day intervention in which they had consumed an especially formulated muffin and beverage containing Brosimum alicastrum Sw. seed flour. A large interindividual variation was observed regarding the amino acids and phenolic metabolites identified in the plasma samples, before and after the intervention. Three phenolic metabolites were significantly increased in the population after the intervention: protocatechuic acid, 5-(methoxy-4'-hydroxyphenyl) valerolactone, and phloretic acid. These metabolites, as well as others that were not significantly increased (although they did increase in several individuals), are probably the product of the microbiota metabolism of the major phenolic compounds present in the B. alicastrum Sw. seed flour and other food ingredients. A significant decrease in 4-ethyl-phenol, a biomarker of stress, was observed in the samples. Results showed that the incorporation of foods rich in phenolic compounds into the regular diet of older adults contributes to the increase in bioactive compounds in plasma, that could substantially benefit their mental, cardiovascular, and digestive health.

Development and Psychometric Evaluation of the End-of-Life Nursing Competency Scale for Clinical Nurses.

This study aimed to develop and establish psychometric properties of the End-of-Life Nursing Competency Scale for Clinical Nurses. The initial items were derived from an in-depth literature review and field interviews. The content validation of these items was assessed over three rounds by experts in end-of-life nursing care. The study included 437 clinical nurses from four hospitals in S, E, and D cities in South Korea. The final exploratory factor analysis resulted in a scale consisting of 21 items with the following five factors that explained 68.44% of the total variance: Physical care-imminent end-of-life, legal and administrative processes, psychological care-patient and family, psychological care-nurses' self, and ethical nursing. The final model with these five subscales was validated through confirmatory factor analysis. Both item convergent-discriminant validity and known-group validity, which compared two groups based on clinical experience (p < 0.008) and working department (p < 0.008), were satisfactory. The internal consistency, as measured by Cronbach's α, ranged from 0.62 to 0.89 for the subscales and was 0.91 for the total scale. This scale has been validated as a reliable and effective instrument for clinical nurses to self-assess their end-of-life nursing competencies in a clinical setting.

Individual patterns of functional connectivity in neonates as revealed by surface-based Bayesian modeling.

Resting-state functional connectivity is a widely used approach to study the functional brain network organization during early brain development. However, the estimation of functional connectivity networks in individual infants has been rather elusive due to the unique challenges involved with functional magnetic resonance imaging (fMRI) data from young populations. Here, we use fMRI data from the developing Human Connectome Project (dHCP) database to characterize individual variability in a large cohort of term-born infants (N = 289) using a novel data-driven Bayesian framework. To enhance alignment across individuals, the analysis was conducted exclusively on the cortical surface, employing surface-based registration guided by age-matched neonatal atlases. Using 10 minutes of resting-state fMRI data, we successfully estimated subject-level maps for fourteen brain networks/subnetworks along with individual functional parcellation maps that revealed differences between subjects. We also found a significant relationship between age and mean connectivity strength in all brain regions, including previously unreported findings in higher-order networks. These results illustrate the advantages of surface-based methods and Bayesian statistical approaches in uncovering individual variability within very young populations.

Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area.

Accurate forecast of fine particulate matter (PM2.5) is crucial for city air pollution control, yet remains challenging due to the complex urban atmospheric chemical and physical processes. Recently deep learning has been routinely applied for better urban PM2.5 forecasts. However, their capacity to represent the spatiotemporal urban atmospheric processes remains underexplored, especially compared with traditional approaches such as chemistry-transport models (CTMs) and shallow statistical methods other than deep learning. Here we probe such urban-scale representation capacity of a spatiotemporal deep learning (STDL) model for 24-hour short-term PM2.5 forecasts at six urban stations in Rizhao, a coastal city in China. Compared with two operational CTMs and three statistical models, the STDL model shows its superiority with improvements in all five evaluation metrics, notably in root mean square error (RMSE) for forecasts at lead times within 12 h with reductions of 49.8 % and 47.8 % respectively. This demonstrates the STDL model's capacity to represent nonlinear small-scale phenomena such as street-level emissions and urban meteorology that are in general not well represented in either CTMs or shallow statistical models. This gain of small-scale representation in forecast performance decreases at increasing lead times, leading to similar RMSEs to the statistical methods (linear shallow representations) at about 12 h and to the CTMs (mesoscale representations) at 24 h. The STDL model performs especially well in winter, when complex urban physical and chemical processes dominate the frequent severe air pollution, and in moisture conditions fostering hygroscopic growth of particles. The DL-based PM2.5 forecasts align with observed trends under various humidity and wind conditions. Such investigation into the potential and limitations of deep learning representation for urban PM2.5 forecasting could hopefully inspire further fusion of distinct representations from CTMs and deep networks to break the conventional limits of short-term PM2.5 forecasts.

Re-evaluating Methods for Assessing Differences in Response in Ileal vs. Colonic Crohn's Disease: A Post-hoc Analysis of the FITZROY Trial.

BACKGROUND AND AIMS: The ileum is the most commonly affected segment of the gastrointestinal tract in Crohn's disease (CD). We aimed to determine whether disease location affects response to filgotinib, a Janus kinase (JAK) inhibitor, in patients with moderate-to-severely active Crohn's disease (CD) and applying appropriate methods to account for differences in measuring disease activity in the ileum compared to the colon. METHODS: This post-hoc analysis of data from the FITZROY phase 2 trial (NCT02048618) compared changes in the Crohn's Disease Activity Index (CDAI) and Simple Endoscopic Score for Crohn's Disease (SES-CD) amongst patients with ileal-dominant and isolated colonic CD treated with 10 weeks of filgotinib 200 mg daily or placebo. A mixed effects model for repeated measures was used to test whether ileal disease responded differently than colonic disease, by evaluating for effect modification using the interaction term of treatment assignment-by-disease location. RESULTS: Numerically greater proportions of patients with isolated colonic disease compared to ileal-dominant CD achieved clinical remission (CDAI <150, 75.9% vs. 41.6%) and endoscopic response (SES-CD reduction by 50%, 52.5% vs. 15.5%) at Week 10. However, after adjusting for baseline disease activity by disease location and within-patient clustering effects, there was no significant difference in treatment response by disease location (mean difference in ΔCDAI between ileal-dominant vs. isolated colonic disease +9.24 [95% CI: -87.19, +105.67], p=0.85; mean difference in ΔSES-CD -1.93 [95% CI: -7.03, +3.44], p=0.48). CONCLUSIONS: Filgotinib demonstrated similar efficacy in ileal-dominant and isolated colonic CD when controlling for baseline disease activity and clustering effects.

Analytic Methods for Understanding the Temporal Patterning of Dietary and 24-H Movement Behaviors: A Scoping Review.

Dietary and movement behaviors [physical activity (PA), sedentary behavior (SED), and sleep] occur throughout a 24-h day and involve multiple contexts. Understanding the temporal patterning of these 24-h behaviors and their contextual determinants is key to determining their combined effect on health. A scoping review was conducted to identify novel analytic methods for determining temporal behavior patterns and their contextual correlates. We searched Embase, ProQuest, and EBSCOhost databases in July 2022 to identify studies published between 1997 and 2022 on temporal patterns and their contextual correlates (e.g., locational, social, environmental, personal). We included 14 studies after title and abstract (n = 33,292) and full-text (n = 135) screening, of which 11 were published after 2018. Most studies (n = 4 in adults; n = 5 in children and adolescents), examined waking behavior patterns (i.e., both PA and SED) of which 3 also included sleep and 6 included contextual correlates. PA and diet were examined together in only 1 study of adults. Contextual correlates of dietary, PA, and sleep temporal behavior patterns were also examined. Machine learning with various clustering algorithms and model-based clustering techniques were most used to determine 24-h temporal behavior patterns. Although the included studies used a diverse range of methods, behavioral variables, and assessment periods, results showed that temporal patterns characterized by high SED and low PA were linked to poorer health outcomes, than those with low SED and high PA. This review identified temporal behavior patterns, and their contextual correlates, which were associated with adiposity and cardiometabolic disease risk, suggesting these methods hold promise for the discovery of holistic lifestyle exposures important to health. Standardized reporting of methods and patterns and multidisciplinary collaboration among nutrition, PA, and sleep researchers; statisticians; and computer scientists were identified as key pathways to advance future research on temporal behavior patterns in relation to health.

An analytical review on the use of artificial intelligence and machine learning in diagnosis, prediction, and risk factor analysis of multiple sclerosis.

Medical research offers potential for disease prediction, like Multiple Sclerosis (MS). This neurological disorder damages nerve cell sheaths, with treatments focusing on symptom relief. Manual MS detection is time-consuming and error prone. Though MS lesion detection has been studied, limited attention has been paid to clinical analysis and computational risk factor prediction. Artificial intelligence (AI) techniques and Machine Learning (ML) methods offer accurate and effective alternatives to mapping MS progression. However, there are challenges in accessing clinical data and interdisciplinary collaboration. By analyzing 103 papers, we recognize the trends, strengths and weaknesses of AI, ML, and statistical methods applied to MS diagnosis. AI/ML-based approaches are suggested to identify MS risk factors, select significant MS features, and improve the diagnostic accuracy, such as Rule-based Fuzzy Logic (RBFL), Adaptive Fuzzy Inference System (ANFIS), Artificial Neural Network methods (ANN), Support Vector Machine (SVM), and Bayesian Networks (BNs). Meanwhile, applications of the Expanded Disability Status Scale (EDSS) and Magnetic Resonance Imaging (MRI) can enhance MS diagnostic accuracy. By examining established risk factors like obesity, smoking, and education, some research tackled the issue of disease progression. The performance metrics varied across different aspects of MS studies: Diagnosis: Sensitivity ranged from 60 % to 98 %, specificity from 60 % to 98 %, and accuracy from 61 % to 97 %. Prediction: Sensitivity ranged from 76 % to 98 %, specificity from 65 % to 98 %, and accuracy from 62 % to 99 %. Segmentation: Accuracy ranged up to 96.7 %. Classification: Sensitivity ranged from 78 % to 97.34 %, specificity from 65 % to 99.32 %, and accuracy from 71 % to 97.94 %. Furthermore, the literature shows that combining techniques can improve efficiency, exploiting their strengths for better overall performance.

Statistical Analysis Methods and Reporting of Patient-Reported Outcomes in Randomized Controlled Trials for Cancer Conducted in Japan: A Systematic Review.

The Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints Data (SISAQOL) initiative was established in 2016 to assess the quality and standardization of patient-reported outcomes (PRO) data analysis in randomized controlled trials (RCTs) on advanced breast cancer. The initiative identified deficiencies in PRO data reporting, including nonstandardized methods for handling missing data. This study evaluated the reporting of health-related quality of life (HRQOL) in Japanese cancer RCTs to provide insights into the state of PRO reporting in Japan. The study reviewed PubMed articles published from 2010 to 2018. Eligible studies included Japanese cancer RCTs with ≥50 adult patients (≥50% were Japanese) with solid tumors receiving anticancer treatments. The evaluation criteria included clarity of the HRQOL hypotheses, multiplicity testing, primary analysis methods, and reporting of clinically meaningful differences. Twenty-seven HRQOL trials were identified. Only 15% provided a clear HRQOL hypothesis, and 63% examined multiple HRQOL domains without adjusting for multiplicity. Model-based methods were the most common statistical methods for the primary HRQOL analysis. Only 22% of the trials explicitly reported clinically meaningful differences in HRQOL. Baseline assessments were reported in most trials, but only 26% reported comparisons between the treatment groups. HRQOL analysis was based on the intention-to-treat population in 19% of the trials, and 74% reported compliance at follow-up; however, 41% did not specify how missing values were handled. Although the rates of reporting clinical hypotheses and clinically meaningful differences were relatively low, the current state of HRQOL evaluation in the Japanese cancer RCT appears comparable to that of previous studies.

Internal validation of Automated Visual Evaluation (AVE) on smartphone images for cervical cancer screening in a prospective study in Zambia.

OBJECTIVES: Visual inspection with acetic acid (VIA) is a low-cost approach for cervical cancer screening used in most low- and middle-income countries (LMICs) but, similar to other visual tests, is subjective and requires sustained training and quality assurance. We developed, trained, and validated an artificial-intelligence-based "Automated Visual Evaluation" (AVE) tool that can be adapted to run on smartphones to assess smartphone-captured images of the cervix and identify precancerous lesions, helping augment VIA performance. DESIGN: Prospective study. SETTING: Eight public health facilities in Zambia. PARTICIPANTS: A total of 8204 women aged 25-55. INTERVENTIONS: Cervical images captured on commonly used low-cost smartphone models were matched with key clinical information including human immunodeficiency virus (HIV) and human papillomavirus (HPV) status, plus histopathology analysis (where applicable), to develop and train an AVE algorithm and evaluate its performance for use as a primary screen and triage test for women who are HPV positive. MAIN OUTCOME MEASURES: Area under the receiver operating curve (AUC); sensitivity; specificity. RESULTS: As a general population screening tool for cervical precancerous lesions, AVE identified cases of cervical precancerous and cancerous (CIN2+) lesions with high performance (AUC = 0.91, 95% confidence interval [CI] = 0.89-0.93), which translates to a sensitivity of 85% (95% CI = 81%-90%) and specificity of 86% (95% CI = 84%-88%) based on maximizing the Youden's index. This represents a considerable improvement over naked eye VIA, which as per a meta-analysis by the World Health Organization (WHO) has a sensitivity of 66% and specificity of 87%. For women living with HIV, the AUC of AVE was 0.91 (95% CI = 0.88-0.93), and among those testing positive for high-risk HPV types, the AUC was 0.87 (95% CI = 0.83-0.91). CONCLUSIONS: These results demonstrate the feasibility of utilizing AVE on images captured using a commonly available smartphone by nurses in a screening program, and support our ongoing efforts for moving to more broadly evaluate AVE for its clinical sensitivity, specificity, feasibility, and acceptability across a wider range of settings. Limitations of this study include potential inflation of performance estimates due to verification bias (as biopsies were only obtained from participants with visible aceto-white cervical lesions) and due to this being an internal validation (the test data, while independent from that used to develop the algorithm was drawn from the same study).

Prenatal air pollution and children's autism traits score: Examination of joint associations with maternal intake of vitamin D, methyl donors, and polyunsaturated fatty acids using mixture methods.

Background: Maternal nutrient intake may moderate associations between environmental exposures and children's neurodevelopmental outcomes, but few studies have assessed joint effects. We aimed to evaluate whether prenatal nutrient intake influences the association between air pollutants and autism-related trait scores. Methods: We included 126 participants from the EARLI (Early Autism Risk Longitudinal Investigation, 2009-2012) cohort, which followed US pregnant mothers who previously had a child with autism. Bayesian kernel machine regression and traditional regression models were used to examine joint associations of prenatal nutrient intake (vitamins D, B12, and B6; folate, choline, and betaine; and total omega 3 and 6 polyunsaturated fatty acids, reported via food frequency questionnaire), air pollutant exposure (particulate matter <2.5 µm [PM2.5], nitrogen dioxide [NO2], and ozone [O3], estimated at the address level), and children's autism-related traits (measured by the Social Responsiveness Scale [SRS] at 36 months). Results: Most participants had nutrient intakes and air pollutant exposures that met US standards. Bayesian kernel machine regression mixture models and traditional regression models provided little evidence of individual or joint associations of nutrients and air pollutants with SRS scores or of an association between the overall mixture and SRS scores. Conclusion: In this cohort with a high familial likelihood of autism, we did not observe evidence of joint associations between air pollution exposures and nutrient intake with autism-related traits. Future work should examine the use of these methods in larger, more diverse samples, as our results may have been influenced by familial liability and/or relatively high nutrient intakes and low air pollutant exposures.

Addressing missing outcome data in randomised controlled trials: A methodological scoping review.

BACKGROUND: Missing outcome data is common in trials, and robust methods to address this are needed. Most trial reports currently use methods applicable under a missing completely at random assumption (MCAR), although this strong assumption can often be inappropriate. OBJECTIVE: To identify and summarise current literature on the analytical methods for handling missing outcome data in randomised controlled trials (RCTs), emphasising methods appropriate for data missing at random (MAR) or missing not at random (MNAR). STUDY DESIGN AND SETTING: We conducted a methodological scoping review and identified papers through searching four databases (MEDLINE, Embase, CENTRAL, and CINAHL) from January 2015 to March 2023. We also performed forward and backward citation searching. Eligible papers discussed methods or frameworks for handling missing outcome data in RCTs or simulation studies with an RCT design. RESULTS: From 1878 records screened, our search identified 101 eligible papers. 90 (89%) papers described specific methods for addressing missing outcome data and 11 (11%) described frameworks for overall methodological approach. Of the 90 methods papers, 30 (33%) described methods under the MAR assumption, 48 (53%) explored methods under the MNAR assumption and 11 (12%) discussed methods under a hybrid of MAR and MNAR assumptions. Control-based methods under the MNAR assumption were the most common method explored, followed by multiple imputation under the MAR assumption. CONCLUSION: This review provides guidance on available analytic approaches for handling missing outcome data, particularly under the MNAR assumption. These findings may support trialists in using appropriate methods to address missing outcome data.

Laboratory Tests on the Possibility of Using Flax Fibers as a Plant-Origin Reinforcement Component in Composite Friction Materials for Vehicle Braking Systems.

Braking systems are extremely important in any vehicle. They convert the kinetic energy of motion into thermal energy that is dissipated into the atmosphere. Different vehicle groups have different nominal and maximum speeds and masses, so the amount of thermal energy that needs to be absorbed by the friction pads and then dissipated can vary significantly. Conventional friction materials are composite materials capable of withstanding high temperatures (in the order of 500-600 °C) and high mechanical loads resulting from braking intensity and vehicle weight. In small vehicles traveling at low speeds, where both the amount of thermal energy and its density are limited, the use of slightly weaker friction materials with better ecological properties can be considered. This work proposes a prototype composite friction material using flax fibers as reinforcement instead of the commonly used aramid. A number of samples were prepared and subjected to laboratory tests. The samples were prepared using components of plant origin, specifically flax fibers. This component acted as reinforcement in the composite friction material, replacing aramid commonly used for this purpose. The main tribological characteristics were determined, such as the values of the coefficients of friction and the coefficients of abrasive wear rate. For this purpose, an authorial method using ball-cratering contact was used. The results were analyzed using statistical methods. It was found that the composite material using flax fibers does not differ significantly in its tribological properties from conventional solutions; so, it can be assumed that it can be used in the vehicle's braking system.

Greedy Ensemble Hyperspectral Anomaly Detection.

Hyperspectral images include information from a wide range of spectral bands deemed valuable for computer vision applications in various domains such as agriculture, surveillance, and reconnaissance. Anomaly detection in hyperspectral images has proven to be a crucial component of change and abnormality identification, enabling improved decision-making across various applications. These abnormalities/anomalies can be detected using background estimation techniques that do not require the prior knowledge of outliers. However, each hyperspectral anomaly detection (HS-AD) algorithm models the background differently. These different assumptions may fail to consider all the background constraints in various scenarios. We have developed a new approach called Greedy Ensemble Anomaly Detection (GE-AD) to address this shortcoming. It includes a greedy search algorithm to systematically determine the suitable base models from HS-AD algorithms and hyperspectral unmixing for the first stage of a stacking ensemble and employs a supervised classifier in the second stage of a stacking ensemble. It helps researchers with limited knowledge of the suitability of the HS-AD algorithms for the application scenarios to select the best methods automatically. Our evaluation shows that the proposed method achieves a higher average F1-macro score with statistical significance compared to the other individual methods used in the ensemble. This is validated on multiple datasets, including the Airport-Beach-Urban (ABU) dataset, the San Diego dataset, the Salinas dataset, the Hydice Urban dataset, and the Arizona dataset. The evaluation using the airport scenes from the ABU dataset shows that GE-AD achieves a 14.97% higher average F1-macro score than our previous method (HUE-AD), at least 17.19% higher than the individual methods used in the ensemble, and at least 28.53% higher than the other state-of-the-art ensemble anomaly detection algorithms. As using the combination of greedy algorithm and stacking ensemble to automatically select suitable base models and associated weights have not been widely explored in hyperspectral anomaly detection, we believe that our work will expand the knowledge in this research area and contribute to the wider application of this approach.

A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures.

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

Comparative effectiveness and safety outcomes between denosumab and bisphosphonate in South Korea.

Both bisphosphonates and denosumab are the mainstays of treatment for osteoporosis to prevent fractures. However, there are still few trials directly comparing the prevention of fractures and the safety of 2 drugs in the treatment of osteoporosis. We aimed to compare the efficacy and safety between denosumab and bisphosphonates using a nationwide claims database. The database was covered with 10 million, 20% of the whole Korean population sampled by age and sex stratification of the Health Insurance Review and Assessment Service in South Korea. Among 228 367 subjects who were over 50 yr of age and taking denosumab or bisphosphonate from January 2018 to April 2022, the analysis was performed on 91 460 subjects after 1:1 propensity score matching. The primary outcome was treatment effectiveness; total fracture, major osteoporotic fracture, femur fracture, pelvic fracture, vertebral fracture, adverse drug reactions; acute kidney injury, chronic kidney disease, and atypical femoral fracture. Total fracture and osteoporotic major fracture, as the main outcomes of efficacy, were comparable in the denosumab and bisphosphonate group (HR 1.06, 95% CI, 0.98-1.15, P = .14; HR 1.13, 95% CI, 0.97-1.32, P = .12, respectively). Safety for acute kidney injury, chronic kidney disease, and atypical femoral fracture also did not show any differences between the 2 groups. In subgroup analysis according to ages, the denosumab group under 70 yr of age had a significantly lower risk for occurrences of acute kidney injury compared to the bisphosphonate group under 70 yr of age (HR 0.53, 95% CI, 0.29-0.93, P = .03). In real-world data reflecting clinical practice, denosumab and bisphosphonate showed comparable effectiveness for total fractures and major osteoporosis fractures, as well as safety regarding acute kidney injury, chronic kidney disease, and atypical femoral fracture.

This study compared the effectiveness and safety of denosumab and bisphosphonates, 2 primary treatments for osteoporosis, using a large South Korean nationwide claims database. Analysis of data from 91 460 individuals over 50 yr old showed no significant difference in preventing fractures or in safety outcomes such as kidney injury and atypical femoral fractures between the 2 drugs. However, among patients under 70, denosumab was associated with a lower risk of acute kidney injury. Overall, both medications demonstrated similar effectiveness and safety in the real-world treatment of osteoporosis.

Comparative effectiveness of denosumab vs alendronate among postmenopausal women with osteoporosis.

Although clinical trials have shown that denosumab significantly increases bone mineral density at key skeletal sites more than oral bisphosphonates, evidence is lacking from head-to-head randomized trials evaluating fracture outcomes. This retrospective cohort study uses administrative claims data from Medicare fee-for service beneficiaries to evaluate the comparative effectiveness of denosumab vs alendronate in reducing fracture risk among women with PMO in the US. Women with PMO ≥ 66 yr of age with no prior history of osteoporosis treatment, who initiated denosumab (n = 89 115) or alendronate (n = 389 536) from 2012 to 2018, were followed from treatment initiation until the first of a specific fracture outcome, treatment discontinuation or switch, end of study (December 31, 2019), or other censoring criteria. A doubly robust inverse-probability of treatment and censoring weighted function was used to estimate the risk ratio associated with the use of denosumab compared with alendronate for hip, nonvertebral (NV; includes hip, humerus, pelvis, radius/ulna, other femur), non-hip nonvertebral (NHNV), hospitalized vertebral (HV), and major osteoporotic (MOP; consisting of NV and HV) fractures. Overall, denosumab reduced the risk of MOP by 39%, hip by 36%, NV by 43%, NHNV by 50%, and HV fractures by 30% compared with alendronate. Denosumab reduced the risk of MOP fractures by 9% at year 1, 12% at year 2, 18% at year 3, and 31% at year 5. An increase in the magnitude of fracture risk reduction with increasing duration of exposure was also observed for other NV fracture outcomes. In this cohort of almost half-a-million treatment-naive women with PMO, we observed clinically significant reductions in the risk of MOP, hip, NV, NHNV, and HV fractures for patients on denosumab compared with alendronate. Patients who remained on denosumab for longer periods of time experienced greater reductions in fracture risk.

Osteoporosis-related fractures can have a significant impact on the health and quality of life of women with postmenopausal osteoporosis, as well as pose a significant burden to society. Although clinical trials have shown that denosumab is more effective at increasing bone mineral density compared with alendronate, there is a lack of evidence evaluating the fracture risk between these 2 commonly used osteoporosis therapies. In this study using Medicare claims data for almost 500 000 women with postmenopausal osteoporosis with no prior history of osteoporosis medication use, we compared the risk of fracture­an important outcome to patients and health care providers­between denosumab and alendronate. Advanced analytic methods were implemented to ensure the study results were valid and were not unduly influenced by biases common in observational studies. We observed clinically meaningful reductions (from 30% up to 50%) in the risk of hip, nonvertebral, non-hip nonvertebral, hospitalized vertebral, and major osteoporotic fractures for patients treated with denosumab compared with alendronate. Patients who remained on denosumab for longer periods of time experienced greater reductions in fracture risk than those who remained on alendronate.