Real-world Data Research Articles

Real-world efficacy of second-line therapies for Helicobacter pylori: a population-based study.

With the increasing prevalence of antibiotic resistance, real-world data on the optimal empirical second-line therapy for Helicobacter pylori are still limited. To evaluate the real-world efficacy of various second-line therapies for H. pylori. This was a retrospective population-based cohort study of all H. pylori-infected patients who had received the second-line treatment after the failure of primary clarithromycin triple therapy in Hong Kong between 2003 and 2018. The retreatment success rates of different second-line therapies were evaluated. A total of 7591 patients who received second-line treatment were included. Notably, the most commonly prescribed regimen was still clarithromycin triple therapy, but the frequency of use had decreased from 59.5% in 2003-06 to 28.7% in 2015-18. Concomitant non-bismuth quadruple therapy had emerged as the commonest regimen (from 3.3% to 43.9%). In a validation analysis, the sensitivity and specificity of retreatment-inferred second-line treatment failure were 88.3% and 97.1%, respectively. The overall success rate of second-line therapies was 73.6%. Bismuth quadruple therapy had the highest success rate of 85.6%, while clarithromycin triple therapy had the lowest success rate of 63.5%. Specifically, bismuth/metronidazole/tetracycline quadruple, metronidazole/tetracycline triple, levofloxacin/metronidazole/tetracycline quadruple, rifabutin/amoxicillin triple and amoxicillin/levofloxacin triple therapies had relatively higher success rates over 80%. Age, treatment duration, baseline conditions and first-line treatment used were associated with success rate. Bismuth quadruple therapy was the most effective second-line regimen for H. pylori in this real-world study. Despite a very low success rate, clarithromycin-containing triple therapies were still commonly used as second-line regimens.

Treat-and-Extend Versus Pro re nata Regimens of Ranibizumab and Aflibercept in Neovascular Age-Related Macular Degeneration: A Comparative Study from Routine Clinical Practice.

Anti-vascular endothelial growth factor (VEGF) is generally given using pro re nata or "treat-and-extend" (T&E) regimens for neovascular age-related macular degeneration (nAMD). Randomized clinical trials have reported that T&E is superior to Pro re nata (PRN), but results from clinical trials may not always be replicated in clinical practice. Real-world data comparing T&E and PRN regimens for nAMD are limited. The objective of this work was to report 24-month outcomes of PRN versus T&E regimens for ranibizumab and aflibercept to treat nAMD in routine clinical practice. We conducted a retrospective analysis of data from a prospectively designed observational outcomes registry, the Fight Retinal Blindness! Project (FRB). Treatment-naïve eyes starting nAMD treatment with at least three injections using a T&E or PRN regimen were tracked by using the FRB. The primary outcome was the mean change in visual acuity (VA) measured by the number of letters read on a logarithm of the minimum angle of resolution chart at 2years versus baseline. The secondary outcome was the number of injections at 2years. From January 1, 2015 to January 31, 2019, 3313 eyes from 2948 patients with nAMD were included: 1243 eyes from 1065 patients were classified as PRN and 2070 eyes from 1935 patients started a T&E regimen. At 24months, patients on the T&E regimen experienced significantly greater mean (95% confidence interval) improvement in VA than those on PRN (+ 4.2 [3.1, 5.2] vs. + 1.3 [0.1, 2.6] letters; p < 0.001), with more injections (14.9 standard deviation(SD) 4.3) vs. 9.8(SD 4.3); p < 0.001). Eyes treated with a T&E regimen had better VA outcomes from VEGF inhibitors than eyes treated PRN. This large real-world data assessment supports previous data from randomized clinical trials that the T&E regimen delivers better outcomes than PRN.

Comparison of the Performance of Structural Break Tests in Stationary and Nonstationary Series: A New Bootstrap Algorithm

AbstractStructural breaks are considered as permanent changes in the series mainly because of shocks, policy changes, and global crises. Hence, making estimations by ignoring the presence of structural breaks may cause the biased parameter value. In this context, it is vital to identify the presence of the structural breaks and the break dates in the series to prevent misleading results. Accordingly, the first aim of this study is to compare the performance of unit root with structural break tests allowing a single break and multiple structural breaks. For this purpose, firstly, a Monte Carlo simulation study has been conducted through using a generated homoscedastic and stationary series in different sample sizes to evaluate the performances of these tests. As a result of the simulation study, Zivot and Andrews (J Bus Econ Stat 20(1):25–44, 1992) are the best-performing tests in capturing a single break. The most powerful tests for the multiple break setting are those developed by Kapetanios (J Time Ser Anal 26(1):123–133, 2005) and Perron (Palgrave Handb Econom 1:278–352, 2006). A new Bootstrap algorithm has been proposed along with the study’s primary aim. This newly proposed Bootstrap algorithm calculates the optimal number of statistically significant structural breaks under more general assumptions. Therefore, it guarantees finding an accurate number of optimal breaks in real-world data. In the empirical part, structural breaks in the real interest rate data of the US and Australia resulting from policy changes have been examined. The results concluded that the bootstrap sequential break test is the best-performing approach due to the general assumption made to cover real-world data.

Artificial intelligence facilitates clinical management of epithelial dysplasia in multiple organs

Epithelial dysplasia is a condition characterized by a spectrum of architectural and cytological alterations to the epithelium, resulting from the accumulation of genetic alterations. It is associated with an increased risk of cancer progression in a variety of organs. However, the variability of different grading systems, as well as inter- and intra-examiner variability, gives rise to concerns regarding the reliability of the results. Histopathology represents the gold standard for the diagnosis of epithelial dysplasia. The combination of big data in pathology and artificial intelligence (AI) will facilitate the achievement of accurate diagnoses and treatments, providing objective and efficient methods to integrate and refine diverse morphological, molecular, and multi-omics information. This perspective provides a summary of the existing research and prospects for the application of AI to epithelial dysplasia in multiple organs. A number of studies have been conducted with the aim of developing a grading system and prognostic identification method for epithelial dysplasia in the oral cavity, larynx, esophagus, and stomach. Digital pathology-based AI may prove useful in facilitating the clinical management of epithelial dysplasia in multiple organs. In summary, digital pathology images obtained by scanning hematoxylin &amp; eosin-stained slides, identifying image features, and building AI models using deep learning combined with machine learning algorithms, validated with real-world data from multicenter cohorts could provide AI as a promising clinical application in the future.

A real-world pharmacovigilance study of FDA adverse event reporting system events for atogepant

ABSTRACT Background Atogepant, an orally administered, small-molecule, calcitonin gene-related peptide (CGRP) receptor antagonist, is being investigated for the treatment of migraine. Methods We collected data from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database.Four algorithms (ROR, PRR, BCPNN, and EBGM) were used as measures to detect signals of atogepant-associated adverse events (AEs) in real-world data. Result Of the 3,552,072 reports, 2876 expressly stated the use of atogepant. Women accounted for the majority of adverse events (AEs), with a notable age concentration of 45–65 years. The percentage of reported adverse events was the highest in the United States.Significant system organ categories (SOC) included nervous system disorders, gastrointestinal disorders, nervous system disorders, surgical and medical procedures, ear and labyrinth disorders. Notably, preferred terms (PTs) related to atogepant include migraine, constipation, nausea, vertigo, somnolence, decreased appetite, dizziness and fatigue. Unexpected adverse events such as abnormal dreams, self-injurious ideation, brain fog, tension headache, nightmare, brain neoplasm, feeling abnormal, euphoric mood, hyperacusis and post concussion syndrome were also identified. Conclusions The present investigation has detected new and unexpected signals of atogepant-related adverse drug reactions (ADRs). In order to confirm these solve safety issues that were previously overlooked, more research is necessary.

A real-world pharmacovigilance study of FDA adverse event reporting system events for atogepant

ABSTRACT Background Atogepant, an orally administered, small-molecule, calcitonin gene-related peptide (CGRP) receptor antagonist, is being investigated for the treatment of migraine. Methods We collected data from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database.Four algorithms (ROR, PRR, BCPNN, and EBGM) were used as measures to detect signals of atogepant-associated adverse events (AEs) in real-world data. Result Of the 3,552,072 reports, 2876 expressly stated the use of atogepant. Women accounted for the majority of adverse events (AEs), with a notable age concentration of 45–65 years. The percentage of reported adverse events was the highest in the United States.Significant system organ categories (SOC) included nervous system disorders, gastrointestinal disorders, nervous system disorders, surgical and medical procedures, ear and labyrinth disorders. Notably, preferred terms (PTs) related to atogepant include migraine, constipation, nausea, vertigo, somnolence, decreased appetite, dizziness and fatigue. Unexpected adverse events such as abnormal dreams, self-injurious ideation, brain fog, tension headache, nightmare, brain neoplasm, feeling abnormal, euphoric mood, hyperacusis and post concussion syndrome were also identified. Conclusions The present investigation has detected new and unexpected signals of atogepant-related adverse drug reactions (ADRs). In order to confirm these solve safety issues that were previously overlooked, more research is necessary.

Comparison of the Performance of Structural Break Tests in Stationary and Nonstationary Series: A New Bootstrap Algorithm

AbstractStructural breaks are considered as permanent changes in the series mainly because of shocks, policy changes, and global crises. Hence, making estimations by ignoring the presence of structural breaks may cause the biased parameter value. In this context, it is vital to identify the presence of the structural breaks and the break dates in the series to prevent misleading results. Accordingly, the first aim of this study is to compare the performance of unit root with structural break tests allowing a single break and multiple structural breaks. For this purpose, firstly, a Monte Carlo simulation study has been conducted through using a generated homoscedastic and stationary series in different sample sizes to evaluate the performances of these tests. As a result of the simulation study, Zivot and Andrews (J Bus Econ Stat 20(1):25–44, 1992) are the best-performing tests in capturing a single break. The most powerful tests for the multiple break setting are those developed by Kapetanios (J Time Ser Anal 26(1):123–133, 2005) and Perron (Palgrave Handb Econom 1:278–352, 2006). A new Bootstrap algorithm has been proposed along with the study’s primary aim. This newly proposed Bootstrap algorithm calculates the optimal number of statistically significant structural breaks under more general assumptions. Therefore, it guarantees finding an accurate number of optimal breaks in real-world data. In the empirical part, structural breaks in the real interest rate data of the US and Australia resulting from policy changes have been examined. The results concluded that the bootstrap sequential break test is the best-performing approach due to the general assumption made to cover real-world data.

Highly Efficient Back-End-of-Line Compatible Flexible Si-Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision

HighlightsA novel optoelectronic synapse compatible with existing chip technology is demonstrated. This device excels in mimicking memory and learning functions using light, making it ideal for future neuromorphic computing in biomedicine.Our design surpasses previous models with its ability to switch between short-term and long-term memory states using light pulses.Experiments on real-world biomedical data (electroencephalogram, electromyography, electrocardiogram) showed significant improvement in classification accuracy. This highlights the device's potential for advanced physiological signal processing and wearable health monitoring systems.

SIMULATION ANALYSIS TO FORECAST PROGRESS AND OPTIMIZE RESOURCE UTILIZATION FOR MATERIAL TRANSPORTATION ACTIVITIES IN THE CONSTRUCTION PROJECT ON PHU QUY ISLAND

This article presents the main results of using a simulation tool to predict time and optimize the resource utilization for the transportion of materials processes in the construction project on Phu Quy island. A simulation model, which includes activities of material transfer processes, has been established and developed based on the EZStrobe simulation program. Various simulation scenarios with different resource usage options have been analyzed. The simulation results are also compared and evaluated with real-world project data. It indicates that for both productivity, time and cost, the option of using two 50-ton cranes at the dock; two 25-ton cranes at the storage yard and four trucks will be the best solution. The results of the study demonstrated the effectiveness of the simulation program, which may help managers choose the resource allocation plan and apply necessary changes to obtain the optimum results in terms of production and cost efficiency for the transportion of contruction materials on the island.

Comparison of simulation tools for optimizing borehole heat exchanger field operation

Model predictive control (MPC) is a promising approach for optimizing the performance of borehole heat exchangers (BHEs) in ground-source heat pump systems. The central element of MPC is the forward model that predicts the thermal dynamics in the ground. In this work, we validate the prediction accuracy of four BHE modeling approaches against real-world measurement data across various operational events and timescales. We simulate the fluid temperature leaving a BHE using a fully discretized 3-D numerical model, a resistance–capacitance model, a g-function model, and a hybrid model. The simulated temperatures are compared to measured temperatures using three validation metrics that quantify temperature offset, noise, and accuracy. The main reason for a mismatch between measured and modeled temperatures is a temperature offset of the simulated temperature. To remove this effect, the models were calibrated for their most sensitive parameter, the ground temperature, and their prediction accuracy over 4 years was evaluated. Thereby, model calibration seems to be a viable solution to account for an unknown load history. The results show that the resistance–capacitance model provides decent predictions in the short term and the g-function model in the long term. However, both models are strongly dependent on accurate calibration. The hybrid model provides the most accurate short and long-term predictions and is less dependent on calibration. Still, its integration into optimization syntax poses challenges compared to the other models. Although not yet applied in model predictive control, the hybrid model stands out as a promising choice for optimizing BHE field operations across various timescales.

A Study on Graph Optimization Method for GNSS/IMU Integrated Navigation System Based on Virtual Constraints.

In GNSS/IMU integrated navigation systems, factors like satellite occlusion and non-line-of-sight can degrade satellite positioning accuracy, thereby impacting overall navigation system results. To tackle this challenge and leverage historical pseudorange information effectively, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints in the graph model are derived from the satellite's position from the previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint serves as an alternative solution for individual satellites in cases of signal anomalies, thereby ensuring the integrity and continuity of the graph optimization model. Additionally, this paper conducts an analysis of the graph optimization model based on these virtual constraints, comparing it with traditional graph models of GNSS/IMU and SLAM. The marginalization of the graph model involving virtual constraints is analyzed next. The experiment was conducted on a set of real-world data, and the results of the proposed method were compared with tightly coupled Kalman filtering and the original graph optimization method. In instantaneous performance testing, the method maintains an RMSE error within 5% compared with real pseudorange measurement, while in a continuous performance testing scenario with no available GNSS signal, the method shows approximately a 30% improvement in horizontal RMSE accuracy over the traditional graph optimization method during a 10-second period. This demonstrates the method's potential for practical applications.

An uncertainty-aware domain adaptive semantic segmentation framework

Semantic segmentation is significant to realize the scene understanding of autonomous driving. Due to the lack of annotated real-world data, the technology of domain adaptation is applied so that the model is trained on the synthetic data and inferred on the real data. However, this domain gap leads to aleatoric and epistemic uncertainty. These uncertainties link to the potential safety issue of autonomous driving in normal weather and adverse weather. In this study, we explore the scientific problem that has received sparse attention previously. We postulate that the Dual Attention module can mitigate the uncertainty in the task of semantic segmentation and provide some empirical study to validate it. Furthermore, the utilization of Kullback-Leibler divergence (KL divergence) helps the estimation of aleatoric uncertainty and boosts the robustness of the segmentation model. Our empirical study on the diverse datasets of semantic segmentation demonstrates the effectiveness of our method in normal and adverse weather. Our code is available at: https://github.com/liubo629/Seg-Uncertainty-dual-attention.

Pharmacovigilance study of GLP-1 receptor agonists for metabolic and nutritional adverse events

Aims: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are employed extensively in the management of type 2 diabetes and obesity. However, there is a paucity of real-world data on their safety and tolerability for metabolic and nutritional adverse events in large sample populations. This study aimed to analyse the metabolic and nutritional safety signatures of different GLP-1 RAs by exploring the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS).Methods: AEs data were extracted from the FDA Adverse Event Reporting System database for each GLP-1 RA from the time of its launch until the second quarter of 2023. The reported odds ratio (ROR), proportional reporting ratio (PRR), Empirical Bayesian Geometric Mean and Bayesian Confidence Propagation Neural Network were employed to identify AE signals.Results: A system organ class of metabolism and nutrition disorders was employed to filter AE reports, resulting in the identification of 10,450 reports for exenatide, 2,860 reports for liraglutide, 240 reports for albiglutide, 4,847 reports for dulaglutide, 2,905 reports for semaglutide, 1,089 reports for tirzepatide, and 13 reports for lixisenatide. Semaglutide (ROR, 3.34; 95%CI, 3.22), liraglutide (ROR, 2.78; 95%CI, 2.69), and exenatide (ROR, 2.15; 95%CI, 2.11) were associated with metabolism and nutrition disorders. The number of AE signals detected were as follows: albiglutide (n = 1), lixisenatide (n = 2), tirzepatide (n = 11), exenatide (n = 12), liraglutide (n = 16), semaglutide (n = 20), dulaglutide (n = 22). Dehydration was the most frequent AE contributing to serious outcomes for liraglutide (n = 318, 23.93%), dulaglutide (n = 434, 20.90%), semaglutide (n = 370, 25.10%) and tirzepatide (n = 70, 32.86%). The time to onset (TTO) of AE was statistically different between exenatide and the other GLP-1 RAs (p &amp;lt; 0.001), and the Weibull parameters for dehydration for liraglutide, dulaglutide, and semaglutide analyses all showed an early failure-type profile.Conclusion: Our study suggests that exenatide, liraglutide, and semaglutide are more susceptible to metabolic and nutritional AEs than other GLP-1 RAs. Liraglutide, dulaglutide, semaglutide, and tirzepaptide’s potential to induce dehydration, necessitates special attention. Despite certain deficiencies, GLP-1 RAs have considerable potential for the treatment of eating disorders.

A dyeing clustering algorithm based on ant colony path-finding mechanism

Clustering has a wide range of applications in various industries. In different research ideas, bionic algorithms have heuristic help for clustering research. However, most of the clustering algorithms based on bionics often have problems of insufficient information extraction, too many required parameters, and limited application scope. Therefore, these algorithms have lower accuracy than traditional clustering algorithms. To solve these problems, this paper proposes a new ant colony clustering algorithm, which is called Dyeing Clustering Algorithm based on Ant Colony Path-finding. This algorithm combines the idea of dyeing with pheromones for the first time and realizes clustering by using ants to crawl and dye each point, bringing new inspiration to the artificial intelligence research. This can better capture data features, improve fault tolerance and achieve a better clustering effect. In addition, the algorithm is constructed as a dynamic system to deeply mine data information with the strategy of swarm intelligence, including the ant colony generation model, path-finding model, path-finding termination model, and dyeing clustering model. Besides, to evaluate the algorithm, we selected 2 simulation data sets, 5 artificial data sets, 5 real-world data sets, 13 comparison algorithms, and 8 evaluation indexes. From the comprehensive multi-round experiments, seven of the eight evaluation indexes of the Dyeing Clustering Algorithm based on Ant Colony Path-finding algorithm are more advantageous than other comparison algorithms, which demonstrates its enormous potential in industrial applications.

Real-life experiences of switching from intravenous to subcutaneous vedolizumab maintenance therapy in patients with inflammatory bowel disease.

A few prospective cohort studies support the safety of switching from intravenous to subcutaneous administration of vedolizumab during maintenance therapy in patients with inflammatory bowel disease. Real-life data on switching after intravenous induction therapy are lacking. The aim was to obtain real-world data on subcutaneous vedolizumab treatment in patients with inflammatory bowel disease after switching from intravenous vedolizumab induction or maintenance therapy, and to evaluate treatment persistence, safety, and changes in disease activity and serum vedolizumab concentrations. We performed a retrospective registry-based study of inflammatory bowel disease patients who received subcutaneous vedolizumab therapy in two tertiary centres. Altogether, 103 patients (26 Crohn's disease and 77 ulcerative colitis) switching from intravenous maintenance therapy (group 1) and 44 patients (14 and 30, respectively) switching from intravenous induction therapy (group 2) were included. At 6 months from baseline, 90.3% of the patients in group 1 and 90.9% of the patients in group 2 continued on subcutaneous vedolizumab. After the switch in group 1, disease activity remained stable. In group 2, clinical disease activity decreased significantly in ulcerative colitis patients (P = 0.002). The median serum vedolizumab concentration was 34.00 µg/ml during subcutaneous maintenance therapy in group 1, which was significantly higher than the median concentration during intravenous therapy (17.00 µg/ml, P < 0.001), but remained unchanged in group 2 after the switch (31.50 µg/ml). Based on these data, subcutaneous vedolizumab treatment is well-tolerated and the treatment persistence remains high after switching from intravenous to subcutaneous vedolizumab therapy.

Update in venous thromboembolism in cancer: Lessons from multi-centre registries

Cancer-associated thrombosis is a common problem in cancer patients and one of the leading causes of death in this population. Randomised clinical trials have shown that both low-molecular-weight heparins and direct oral anticoagulants are the treatments of choice for cancer-associated thrombosis. Despite this, small sample sizes, poor representation of some patient subgroups and lack of information about real-world clinical situations are some of the limitations of randomised trials. To overcome these problems, registries have been established to collect real-world data from patients with cancer-associated thrombosis, offering new evidence and information to supplement the findings of randomised clinical trials. However, few registries have focused exclusively on cancer patients, and some have excluded various subgroups of patients. Additionally, data collection and processing are another major challenge in analysing registry results, where the emergence of artificial intelligence will play a fundamental role.

A Dyeing Clustering Algorithm based on Ant Colony Path-finding Mechanism

Clustering has a wide range of applications in various industries. In different research ideas, bionic algorithms have heuristic help for clustering research. However, most of the clustering algorithms based on bionics often have problems of insufficient information extraction, too many required parameters, and limited application scope. Therefore, these algorithms have lower accuracy than traditional clustering algorithms. To solve these problems, this paper proposes a new ant colony clustering algorithm, which is called Dyeing Clustering Algorithm based on Ant Colony Path-finding. This algorithm combines the idea of dyeing with pheromones for the first time and realizes clustering by using ants to crawl and dye each point, bringing new inspiration to the artificial intelligence research. This can better capture data features, improve fault tolerance and achieve a better clustering effect. In addition, the algorithm is constructed as a dynamic system to deeply mine data information with the strategy of swarm intelligence, including the ant colony generation model, path-finding model, path-finding termination model, and dyeing clustering model. Besides, to evaluate the algorithm, we selected 2 simulation data sets, 5 artificial data sets, 5 real-world data sets, 13 comparison algorithms, and 8 evaluation indexes. From the comprehensive multi-round experiments, seven of the eight evaluation indexes of the Dyeing Clustering Algorithm based on Ant Colony Path-finding algorithm are more advantageous than other comparison algorithms, which demonstrates its enormous potential in industrial applications.

PRIMCAT-CRC: a data-driven whole disease simulation model for predicting the number of colorectal cancer patients by stage and treatment line in Australia.

Effective healthcare planning, resource allocation, and budgeting require accurate predictions of the number of patients needing treatment at specific cancer stages and treatment lines. The PRedicting the population health economic IMpact of current and new Cancer Treatments Colorectal Cancer (PRIMCAT-CRC) simulation model was developed to meet this requirement for all CRC stages and relevant molecular profiles in Australia. Real-world data was used to estimate treatment utilisation and time-to-event distributions. This populated a discrete-event simulation, projecting the number of patients receiving treatment across all disease stages and treatment lines for CRC and forecasting the number of patients likely to utilise future treatments. Illustrative analyses were undertaken, estimating treatments across disease stages and treatment lines over a 5-year period (2022-2026). We demonstrated the model's applicability through a case study introducing pembrolizumab as a first-line treatment for mismatch-repair deficient stage IV. Clinical registry data from 7,163 patients informed the model. The model forecasts 15,738 incident and 2,821 prevalent cases requiring treatment in 2022, rising to 15,921 and 2,871 respectively by 2026. Projections show that over 2022-2026, there will be a total of 116,752 treatments initiated, with 43% intended for stage IV disease. The introduction of pembrolizumab is projected for 706 patients annually, totalling 3,530 individuals starting treatment with pembrolizumab over the forecasted period, without significantly altering downstream utilisation of subsequent treatments. PRIMCAT-CRC is a versatile tool that can be used to estimate the eligible patient populations for novel cancer therapies, thereby reducing uncertainty for policymakers in decisions to publicly reimburse new treatments.

Causal inference and machine learning in endocrine epidemiology.

With the rapid development of computer science, there is an increasing demand for the use of causal inference methods and machine learning in the research of endocrine disorders and their long-term health outcomes. However, studies on the effective and appropriate applications of these approaches in real-world data and clinical settings are still limited. This review will illustrate the use of causal inference and machine learning in epidemiological research within the field of endocrinology and metabolism. It will examine each concept of causal inference and machine learning through application examples of endocrine disorders. Subsequently, the paper will discuss the integration of machine learning within the causal inference framework, including (i) the estimation of treatment effects or the causal relationship between exposure and outcomes, and (ii) the evaluation of heterogeneity in such treatment effects (or exposure-outcome causal relationship) based on individuals' characteristics. Accurately assessing causal relationships and their heterogeneity across different individuals is crucial not only for determining effective interventions, but also for the appropriate allocation of medical resources and reducing healthcare disparities. By illustrating some application examples in endocrinology, this review aims to enhance readers' understanding and application of causal inference and machine learning in future epidemiological studies focusing on endocrine disorders.

Fusion of multi-resolution data for estimating speed-density relationships

Estimating traffic flow models, such as speed-density relationships, using data from multiple sources with different temporal resolutions is a prevalent challenge encountered in real-world scenarios. The resolution incompatibility is often intuitively addressed by averaging the high-resolution (HR) data to synchronize with the low-resolution (LR) data. This paper shows that ignoring the variability of HR data within the LR interval during the averaging process could lead to systematic data point distortions, resulting in biased model estimations. The average absolute biases of models estimated from the average data increase with the lost variability of HR data within the LR intervals. Subsequently, it proves that for any given complete average data dataset, there must exist an optimal dataset that minimizes the average absolute bias in model estimations introduced by the averaging process. A novel procedure for determining the practical optimal dataset is proposed. To test the proposed method, real-world HR data from four sites in Hong Kong and Nanjing, China were collected to mimic situations with multi-resolution data. Results demonstrated that the proposed method can significantly reduce the average absolute biases of models estimated from the determined practical optimal dataset, as compared to models estimated from the complete average dataset.