Real Data Research Articles

Fast Bayesian Functional Regression for Non-Gaussian Spatial Data

Functional generalized linear models (FGLM) have been widely used to study the relations between non-Gaussian response and functional covariates. However, most existing works for FGLM assume independence among observations and therefore they are of limited applicability for correlated data. A particularly important example is spatial functional data, where we observe functions over spatial domains, such as the age population curve or temperature curve at each areal unit. In this paper, we extend FGLM by incorporating spatial random effects. Especially, we study the relationship between the non-Gaussian response variable and functional covariates that are spatially observed. However, such model has computational and inferential challenges. The high-dimensional spatial random effects cause the slow mixing of Markov chain Monte Carlo (MCMC) algorithms. Furthermore, spatial confounding can lead to bias in parameter estimates and inflate their variances. To address these issues, we propose an efficient Bayesian method using a sparse reparameterization of high-dimensional random effects. We also study the average coverage probabilities of the credible intervals of functional parameters. We apply our methods to simulated and real data examples, including malaria incidence data and US COVID-19 data. The proposed method is fast while providing accurate functional estimates.

Grid-aware Stochastic Game modeling of residential electric flexibility under incentives

We consider a community of households owning electrical assets that can be operated as flexible loads, such as an electric vehicle, a heat pump with thermal storage and a stationary battery. Automated agents are tasked with the management of flexibility under a variable time-of-use tariff with the goal of maximizing final user utility. A population of homogeneous automated agents with similar interests, managing flexibilities of the same nature, runs the risk of stressing the grid through excessively synchronized actions, and this interaction among the agents has to be taken into account. We model this environment as a Stochastic Game, developing a formulation of the state and action spaces and obtaining the transition probability matrix. The probabilities involved are fitted on real or realistic synthetic data. Leveraging the particular structure of the problem, we propose a two-step solution method similar to policy iteration, that proves capable of producing sensible solutions. Finally, we provide an example of flexibility steering through the modulation of the time-of-use tariff, investigating the effectiveness of the incentive depending on its entity and the model parameters that characterize punishment for overcoordination and user satisfaction, and we illustrate how the tool could be employed in a real Energy Community.

Construction of A Geographically Weighted Nonparametric Regression Model Fit Test

One of the approach of Geographically Weighted Regression (GWR) models is the Geographically Weighted Nonparametric Regression (GWNR) has more parameters than the GWR model. Models with more parameters usually have better match values, which is an advantage, while models with fewer parameters have the advantage of being easier to use and interpret. However, a model with more parameters should be used if it is proven to be significantly superior. Therefore, the purpose of this study was to develop a hypothesis test of goodness of fit test for GWNR model. The goodness of fit test was performed for the real data. We found that the GWNR model was more suitable than the mixed nonparametric regression model. Some highlights of the proposed method are:• A new model for GWR to overcome the unknown regression function by using mixed estimator spline truncated and fourier series at nonparametric regression• Goodness of fit for GWNR to testing the model fit between the mixed nonparametric regression model and GWNR• Applied goodness of fit test to poverty data in Sulawesi Island and infant mortality in East Java

Objective Bayesian Meta-Analysis Based on Generalized Marginal Multivariate Random Effects Model

Objective Bayesian inference procedures are derived for the parameters of the multivariate random effects model generalized to elliptically contoured distributions. The posterior for the overall mean vector and the between-study covariance matrix is deduced by assigning two noninformative priors to the model parameter, namely the Berger and Bernardo reference prior and the Jeffreys prior, whose analytical expressions are obtained under weak distributional assumptions. It is shown that the only condition needed for the posterior to be proper is that the sample size is larger than the dimension of the data-generating model, independently of the class of elliptically contoured distributions used in the definition of the generalized multivariate random effects model. The theoretical findings of the paper are applied to real data consisting of ten studies about the effectiveness of hypertension treatment for reducing blood pressure where the treatment effects on both the systolic blood pressure and diastolic blood pressure are investigated.

A Multivariate Mixture Regression Model for Constrained Responses

Compositional data are vectors typically representing proportions of a whole, that is, those whose elements are strictly positive and subject to a unit-sum constraint. The increasing number of fields where this type of data arises makes the development of proper statistical tools an important issue. From a regression perspective, whenever the multivariate response is a compositional vector, a proper model that accounts for the unit-sum constraint is the well-established Dirichlet regression model. However, there are significant drawbacks mainly due to the limited flexibility of the Dirichlet distribution. The aim of this contribution is to introduce a new multivariate regression model for constrained responses, that is based on the extended flexible Dirichlet distribution (which is a structured mixture with Dirichlet distributed components). The new model is obtained by adopting a novel reparameterization which allows for, among other things, the presence of suitably designed cluster-specific regression patterns. It is shown to provide considerably greater flexibility and better performance than the standard Dirichlet regression model. In particular, from theoretical analysis, intensive simulation studies in many challenging scenarios, as well as from a real data application, it emerges that the new regression model can handle several issues affecting the Dirichlet regression, such as the presence of outliers, latent groups, multi-modality, and positive correlations.

Reliability inference for a family of inverted exponentiated distributions under block progressive Type II censoring

In this article, estimation of survival characteristics is discussed when life tests are performed against different test facilities using block progressive Type-II censoring. Under the assumption that the lifetimes of test units follow a family of inverted exponentiated distributions, estimates are derived under classical and hierarchical Bayesian frameworks when the differences in different test facilities are taken into consideration. Maximum likelihood estimators are obtained and their existence and uniqueness properties are also established. In addition, Bayes estimators are provided under a hierarchical framework, and a hybrid Metropolis-Hastings sampling procedure is proposed for complex posterior computation. The performance of all the proposed approaches is compared via extensive simulation studies and two real-life data are analyzed for illustration. From numerical results we observe that the hierarchical approach provides relatively better estimates for model parameters.

Endocrine therapy in advanced high-grade ovarian cancer: real-life data from a multicenter study and a review of the literature.

In women, ovarian cancer is the eighth most frequent cancer in incidence and mortality. It is often diagnosed at advanced stages; relapses are frequent, with a poor prognosis. When platinum resistant, subsequent lines of chemotherapy are of limited effect and often poorly tolerated, leading to quality of life deterioration. Various studies suggest a hormonal role in ovarian carcinogenesis, with a rationale for endocrine therapy in these cancers. This multicenter, retrospective study assessed the use of endocrine treatment for high-grade ovarian epithelial carcinomas treated between 2010 and 2020. Eighty-one patients with ovarian cancers were included. The median duration of platinum sensitivity was 29 months. We observed a 35% disease control rate with endocrine therapy, and 10% reported symptom improvement. For 19 patients (23.5%), the disease was stabilized for more than 6 months. Median overall survival from diagnosis was 62.6 months. Regarding endocrine therapy predictive factors of response, in a multivariate analysis, 3 factors were statistically significant in favoring progression-free survival: platinum sensitivity (P = .021), an R0 surgical resection (P = .020), and the indication for hormone therapy being maintenance therapy (P = .002). This study shows real-life data on endocrine therapy in ovarian cancer. As it is a low-cost treatment with many advantages such as its oral administration and its safety, it may be an option to consider. A perspective lies in the search for cofactors to aim as future therapeutic targets to improve the effectiveness of hormone treatment by means of combination therapy.

Dual Environmental, Social, and Governance (ESG) Index for Corporate Sustainability Assessment Using Blockchain Technology

Investors are increasingly relying on Environmental, Social, and Governance (ESG) indexes to obtain a third-party assessment of corporate sustainability performance. Various ESG indexes are, therefore, released by prominent rating agencies, including MSCI, Sustainalytics, Refinitiv, etc. However, existing ESG indexes overvalue the usage of massive ESG metrics while ignoring various ESG disclosure levels, leading to critical issues such as limited company coverage, inflexible ESG framework, and obscure assessment processes. This paper proposes a novel Dual ESG Index (DESGI) model using blockchain technology to provide a flexible and transparent corporate sustainability assessment. Firstly, the DESGI model is developed by analogy to the rationale and concepts of the academic credit system due to its advantages of scalability and flexibility. Secondly, blockchain is used to build a transparent environment for ESG assessment. Thirdly, the smart contract and crypto token, as the core blockchain constructs, are used to achieve the dual-dimensional ESG depth and width assessment using ESG GPA and ESG credit, respectively. Finally, a case study is carried out to validate the DESGI by using real-life ESG data and comparing it with four existing ESG indexes. Several managerial implications are also found: (1) DESGI can expand the scope of companies evaluated by ESG criteria regardless of company size or scale; (2) DESGI provides a good potential to fight against greenwashing through the blockchain-based traceability; (3) DESGI can identify the ESG elites who disclose fewer ESG metrics but with excellent ESG performances, which can hardly be achieved using traditional ESG indexes.

AI-Driven Synthetization Pipeline of Realistic 3D-CT Data for Industrial Defect Segmentation

Training data is crucial for any artificial intelligence model. Previous research has shown that various methods can be used to enhance and improve AI training data. Taking a step beyond previous research, this paper presents a method that uses AI techniques to generate CT training data, especially realistic, artificial, industrial 3D voxel data. This includes that material as well as realistic internal defects, like pores, are artificially generated. To automate the processes, the creation of the data is implemented in a 3D Data Generation, called SPARC (Synthetized Process Artificial Realistic CT data). The SPARC is built as a pipeline consisting of several steps where different types of AI fulfill different tasks in the process of generating synthetic data. One AI generates geometrically realistic internal defects. Another AI is used to generate a realistic 3D voxel representation. This involves a conversion from STL to voxel data and generating the gray values accordingly. By combining the different AI methods, the SPARC pipeline can generate realistic 3D voxel data with internal defects, addressing the lack of data for various applications. The data generated by SPARC achieved a structural similarity of 98% compared to the real data. Realistic 3D voxel training data can thus be generated. For future AI applications, annotations of various features can be created to be used in both supervised and unsupervised training.

Monitoring of Adverse Events and Safety in Autoinflammatory Diseases: Real-Life Data from the Eurofever Registry.

The study is aimed to evaluate the impact of safety events in the Eurofever registry for Autoinflammatory diseases. This was a retrospective and longitudinal observational multicentre study. Data were retrieved from the international registry Eurofever, starting patients' enrolment since 2009. All moderate, severe, or very severe AEs reported by treating physician in Eurofever were analyzed regardless of a possible suspected causal relationship to any therapies and according to the latest release of the Medical Dictionary for Regulatory Activities. Complete information on safety were available in 2464 patients enrolled in the registry. In 1499 of them retrospective data encompassing the period from disease onset to enrolment were available, whereas 965 consecutive patients entered in the longitudinal part of the study. A total of 479 AEs have been reported in 275 patients. Eighty-two AEs were reported as serious and 99 were drug-related according to the physicians. Infections or infestations (94; 19.6%), gastrointestinal disorders (66; 13.8%), nervous system disorders (41; 8.6%) and systemic disorders or administration site reactions (35; 7.3%) were the most frequent reported events. The highest absolute number of drug-related AEs were related to biologic DMARDs (40/99 reports, 40,4%) and colchicine (31/99 reports, 31.3%). Present study shows the importance of a longitudinal and homogeneous registration of the AEs in rare conditions, with a particular focus on the safety profile of the treatments used in these conditions.

Dynamic Visual Acuity, Vestibulo-Ocular Reflex, and Visual Field in National Football League (NFL) Officiating: Physiology and Visualization Engineering for 3D Virtual On-Field Training

The ability to make on-field, split-second decisions is critical for National Football League (NFL) game officials. Multiple principles in visual function are critical for accuracy and precision of these play calls, including foveation time and unobstructed line of sight, static visual acuity, dynamic visual acuity, vestibulo-ocular reflex, and sufficient visual field. Prior research has shown that a standardized curriculum in these neuro-ophthalmic principles have demonstrated validity and self-rated improvements in understanding, confidence, and likelihood of future utilization by NFL game officials to maximize visual performance during officiating. Virtual reality technology may also be able to help optimize understandings of specific neuro-ophthalmic principles and simulate real-life gameplay. Personal communication between authors and NFL officials and leadership have indicated that there is high interest in 3D virtual on-field training for NFL officiating. In this manuscript, we review the current and past research in this space regarding a neuro-ophthalmic curriculum for NFL officials. We then provide an overview our current visualization engineering process in taking real-life NFL gameplay 2D data and creating 3D environments for virtual reality gameplay training for football officials to practice plays that highlight neuro-ophthalmic principles. We then review in-depth the physiology behind these principles and discuss strategies to implement these principles into virtual reality for football officiating.

A deep learning based communication traffic prediction approach for smart monitoring of distributed energy resources in virtual power plants

AbstractVirtual power plants (VPPs) have been widely recognized as a key enabler for energy system neutrality. The communication traffic of a VPP fundamentally indicates its activeness in interacting with the power system, thus providing a new dimension in depicting the behaviour characteristics of distributed energy resources in VPPs. Therefore, the prediction of communication traffic is significant in improving the control efficiency of VPPs. However, due to the involvement of numerous interactive agents characterised by both individual randomness and coordinative characteristics, traditional prediction models are no longer capable of fitting VPP communication traffic effectively. Therefore, a novel prediction model is introduced that enhances the prediction accuracy by integrating long short‐term memory (LSTM) and variational mode decomposition (VMD). This model employs VMD as the initial step for extracting the intrinsic modes from the traffic sequence, thereby mitigating the impact of incidental noise. Then, LSTM is applied to fit each intrinsic mode individually. Additionally, considering the outer influencing factors, the attention mechanism is incorporated. Finally, all sub‐prediction algorithms are neatly integrated as a whole prediction model. The proposed model is evaluated through simulation prediction using realistic VPP communication traffic data, and the results demonstrate its effectiveness.

Insights for clinical management from the real-life data of the centralized West of Scotland biliary cancer clinic.

With the increasing of novel therapeutics for the treatment of Biliary Tract Cancers (BTC), and the need to assess their socio-economic impacts for national licence approvals, it is as important as ever to have real-life data in national populations. We performed an audit of the first 2 year-activity (Sep 2019-Sep 2021) of the centralized West-of-Scotland-BTC clinic. 122 patients accessed the service, including 68% with cholangiocarcinoma (CCA), 27% with gallbladder cancer (GBC), and 5% with ampulla of Vater carcinoma with biliary phenotype (AVC). Median age at diagnosis was 66 (28-84), with 30% of newly diagnosed patients being younger than 60 years-old. Thirty-five cases (29%) underwent surgery, followed by adjuvant-chemotherapy in 66%. 60% had recurrent disease (80% with distant relapse). Sixty-four patients (58%) started first-line Systemic-AntiCancer-Treatment (SACT). Of these, 37% received second line SACT, the majority of which had iCCA and GBC. Thirty-% of those who progressed received third line SACT. About 30% of BTC were eligible for curative surgery. Fifty-eight and twenty% of the overall cohort of advanced BTC patients received first and second line SACT. Our data suggest that reflex genomic profiling may not be cost-effective until molecularly driven strategies are limited to second line setting.

Application of 3D laser scanning technology for mapping and accuracy assessment of the point cloud model for the Great Achievement Palace heritage building

Heritage buildings represent history and act as vessels of human civilisation. The significance of these architectural phenomena increases as society increases, which renders their preservation and ethical use increasingly crucial. Hence, preserving heritage buildings is necessary for societal benefit. The accuracy of three-dimensional (3D) point cloud models of heritage buildings is important to their digital conservation. Traditional mapping methods typically require more time and human resources. Furthermore, the outcome is subject to measurement omissions, errors, and other issues, which are contrary to the protection of the object measured. The complex elements of ancient Chinese architecture render it challenging for traditional measuring techniques to accurately capture spatial structural information. The 3D laser scanning technology is a novel technology to obtain 3D data rapidly. In this paper, the Great Achievement Palace of the Confucian Temple in Yuci District, Jinzhong City, Shanxi Province, was used as an example to examine the application of 3D laser scanning technology to acquire point cloud models of heritage buildings. Field data collection for architectural heritage using 3D laser scanning technology requires measuring station setting analysis to ensure effect accuracy while considering the elements of target location and quantity. For the 3D point cloud model of the Great Achievement Palace, error analysis and accuracy assessment were conducted on the quality elements of alignment accuracy between scanning stations, point cloud model reliability, point cloud data noise condition, and whether the point cloud data were stratified. The findings demonstrated that the improved method quickly and accurately acquired 3D point cloud model data and obtained realistic spatial data models of the heritage building.

Simulation study to evaluate when Plasmode simulation is superior to parametric simulation in estimating the mean squared error of the least squares estimator in linear regression.

Simulation is a crucial tool for the evaluation and comparison of statistical methods. How to design fair and neutral simulation studies is therefore of great interest for both researchers developing new methods and practitioners confronted with the choice of the most suitable method. The term simulation usually refers to parametric simulation, that is, computer experiments using artificial data made up of pseudo-random numbers. Plasmode simulation, that is, computer experiments using the combination of resampling feature data from a real-life dataset and generating the target variable with a known user-selected outcome-generating model, is an alternative that is often claimed to produce more realistic data. We compare parametric and Plasmode simulation for the example of estimating the mean squared error (MSE) of the least squares estimator (LSE) in linear regression. If the true underlying data-generating process (DGP) and the outcome-generating model (OGM) were known, parametric simulation would obviously be the best choice in terms of estimating the MSE well. However, in reality, both are usually unknown, so researchers have to make assumptions: in Plasmode simulation studies for the OGM, in parametric simulation for both DGP and OGM. Most likely, these assumptions do not exactly reflect the truth. Here, we aim to find out how assumptions deviating from the true DGP and the true OGM affect the performance of parametric and Plasmode simulations in the context of MSE estimation for the LSE and in which situations which simulation type is preferable. Our results suggest that the preferable simulation method depends on many factors, including the number of features, and on how and to what extent the assumptions of a parametric simulation differ from the true DGP. Also, the resampling strategy used for Plasmode influences the results. In particular, subsampling with a small sampling proportion can be recommended.

Rituximab retention rate in systemic sclerosis: a long term real-life multicenter study.

to report real-life data on rituximab retention-rate as indicator of safety and efficacy in a multicentric national cohort of systemic sclerosis patients. SSc patients treated with rituximab and followed for at least 36 months were included, clinically characterized, and longitudinally monitored. A competing risk analysis with sub-Hazard Ratio(sHR) definition was performed to explore the clinical variables linked to specific cause of rituximab discontinuation. One-hundred-fifty-two SSc-patients (mean age 47.3 ± 12.3 years; females 79.6%; diffuse disease 77.6%; anti-topoisomerase-I positivity 63.2%) were evaluated over a median(IQR) time of 3.3(1.7-5.0) years. The primary indication for rituximab were interstitial lung disease (ILD)(38.8%), worsening skin fibrosis(36.8%), and arthritis(13.8%); 138 patients(90.8%) received more than one rituximab course. The 5-years rituximab retention rate was 59.9%(44.6-64.7%). Clinical response was the most common reason for rituximab discontinuation[5.7(3.7-8.4) per 100 patient-year] and was associated with a shorter disease duration[sHR 0.8(0.7-0.9)], anti-topoisomerase-I negativity[sHR 0.4(0.2-0.9)], previous digital ulcers[sHR 2.6(1.1-6.2] and no history of arthritis[sHR 0.3 (0.1-0.8)]. Treatment failure was the second cause of rituximab discontinuation[3.7(2.2-6.0) per 100 patient-year] and was associated with anti-centromere antibody positivity[sHR 2.8(1.1-7.4)] and anti-topoisomerase-I negativity[sHR 0.2(0.1-0.6)]. Adverse events(AEs) were the less common cause of discontinuation[3.1(1.7-5.2) per 100 patient-year], associated with limited cutaneous subset[sHR 3.4(1.2-9.7)] and previous mycophenolate mofetil treatment[sHR 4.5(1.2-16.3)]. rituximab is a safe and effective treatment in SSc: clinical response emerged as the primary reason for rituximab discontinuation, and AEs had a limited impact on treatment persistence. The identification of specific disease features associated with a response to rituximab will be useful in the management of SSc-patients.

Energy Management of Hydrogen Hybrid Electric Vehicles—Online-Capable Control

The results shown in this paper extend our research group’s previous work, which presents the theoretically achievable hydrogen engine-out NO​xeo (H​2-NO​xeo) Pareto front of a hydrogen hybrid electric vehicle (H​2-HEV). While the Pareto front is calculated offline, which requires significant computing power and time, this work presents an online-capable algorithm to tackle the energy management of a H​2-HEV with explicit consideration of the H​2-NO​xeo trade-off. Through the inclusion of realistic predictive data on the upcoming driving mission, a model predictive control algorithm (MPC) is utilized to effectively tackle the conflicting goal of achieving low hydrogen consumption while simultaneously minimizing NO​xeo. In a case study, it is shown that MPC is able to satisfy user-defined NO​xeo limits over the course of various driving missions. Moreover, a comparison with the optimal Pareto front highlights MPC’s ability to achieve close-to-optimal fuel performance for any desired cumulated NO​xeo target on four realistic routes for passenger cars.

Conditional seasonal markov-switching autoregressive model to simulate extreme events: Application to river flow

Extreme events have the potential to significantly impact transportation infrastructure performance. For example, in the case of bridges, climate change impacts the river discharge, hence scouring patterns, which in turn, affects the bridge foundation stability. Therefore, extreme events (river flow) forecasting is mandatory in bridge reliability analysis. This paper approaches this river flow forecasting problem by developing a Markov-Switching Autoregressive model coupled with a conditional hidden seasonal Markov component. In addition, the proposed model is also combined with the deep machine learning neural networks method to forecast river flow from a dataset or from simulations. The proposed method is illustrated by using realistic data: historic river flow values of the Thames River. The results indicate that the proposed model well represented the extreme events within the dataset. In terms of river flow forecasting, the results indicate that the forecasts improve when the training period changes from 20 years to 40 years.

Contrasting Effects of Urban Land Cover Change and Anthropogenic Heat on Summer Precipitation Over the Yangtze River Delta of China: Analyses From an Atmospheric Moisture Budget Perspective

AbstractBoth urban land cover (ULC) change and anthropogenic heat (AH) emission are important causes of urban heat island, but their relative contributions to the changes in urban precipitation and the related mechanism remain unclear. Based on numerical simulations utilizing the latest realistic urban fraction and AH data over the Yangtze River Delta urban agglomeration, we found that ULC and AH resulted in nearly opposite effects on precipitation. Various dynamical and thermodynamic processes were involved according to the atmospheric moisture budget analyses. AH increased precipitation particularly during afternoon, and the increases were stronger during heavy precipitation events because of the enhanced moisture convergence effect together with the release of moisture storage previously accumulated in the atmosphere. Differently, ULC reduced mean precipitation mainly due to suppressed evaporation. During weak precipitation events, the suppressed evaporation was largely balanced by the intensified moisture convergence, but during heavy events, ULC caused more pronounced precipitation reduction because the moisture convergence response disappeared and failed to offset the evaporation effect. The relative contributions of different dynamical and thermodynamic processes such as those related to circulation, moisture gradient, and background moisture availability to the temporal variation in the total moisture convergence were further quantified. Overall, our results help better understand the relative roles of different aspects of urbanization on precipitation, and suggest that compared to ULC, reduction in AH emission that is tightly related to the energy consumption structure could be more efficient for mitigating the risk of extreme precipitation.

An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies

BackgroundA Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have a closed-form likelihood function or parameter solutions, computational methods are conventionally used to approximate the likelihoods and obtain parameter estimates. The most commonly used computational methods are the Iteratively Reweighted Least Squares (IRLS), the Laplace approximation (LA), and the Adaptive Gauss-Hermite quadrature (AGHQ). Despite being widely used, it has not been clear how these computational methods compare and perform in the context of an aggregate data meta-analysis (ADMA) of DTAs.MethodsWe compared and evaluated the performance of three commonly used computational methods for GLMM - the IRLS, the LA, and the AGHQ, via a comprehensive simulation study and real-life data examples, in the context of an ADMA of DTAs. By varying several parameters in our simulations, we assessed the performance of the three methods in terms of bias, root mean squared error, confidence interval (CI) width, coverage of the 95% CI, convergence rate, and computational speed.ResultsFor most of the scenarios, especially when the meta-analytic data were not sparse (i.e., there were no or negligible studies with perfect diagnosis), the three computational methods were comparable for the estimation of sensitivity and specificity. However, the LA had the largest bias and root mean squared error for pooled sensitivity and specificity when the meta-analytic data were sparse. Moreover, the AGHQ took a longer computational time to converge relative to the other two methods, although it had the best convergence rate.ConclusionsWe recommend practitioners and researchers carefully choose an appropriate computational algorithm when fitting a GLMM to an ADMA of DTAs. We do not recommend the LA for sparse meta-analytic data sets. However, either the AGHQ or the IRLS can be used regardless of the characteristics of the meta-analytic data.