Data-generating Process Research Articles

Factors influencing evidence-based cardiovascular disease prevention programming in rural African American communities: a community-engaged concept mapping study

BackgroundAfrican Americans experience cardiovascular disease (CVD) disparities, and the burden is greatest in the rural south. Although evidence-based CVD prevention and management programs have been tailored to this context, implementation has been limited and not sustained long-term. To understand how to implement and sustain evidence-based CVD programs at scale, we must explore the perspectives of organizations serving rural African American communities and situate findings within foundational Implementation Science frameworks.MethodsThis study used group concept mapping (GCM) to elicit and synthesize stakeholder perspectives into an action-focused conceptual model depicting factors influencing implementation of evidence-based CVD programs. Representatives of community-based, faith, and healthcare organizations serving African Americans in five rural North Carolina counties were recruited via purposive sampling techniques. Participants (total n = 31) completed three activities: 1) brainstorming in response to an open-ended prompt (n = 31); 2) sorting brainstorm data into wider concepts and rating each in terms of relative importance and feasibility (n = 26); and 3) collaborative interpretation and refinement of the concept map (n = 19). Multivariate statistical analysis was used to generate a concept map. Absolute pattern matches comparing ratings of the relative importance and feasibility of each factor were generated and depicted via ladder graphs.ResultsThe final concept map included five factors: Accessibility, Community and Social Factors, Education and Training, Financial/Resource Development, and Organization Capacity and Staffing. There was high agreement (r = .98) between ratings of importance and feasibility. Education and Training, both within organizations and the wider community, was rated as the most important and feasible factor and Financial/Resource Development was the least important and feasible.ConclusionsThe concept map emphasizes aspects of organizations (inner setting), their surrounding community (outer setting), and individual stakeholders (participants, implementers) as influencing implementation of evidence-based CVD prevention and management programs in rural African American communities. The nature of the intervention or implementation processes were de-emphasized. Organizations in rural African American communities may feel equipped to implement a range of evidence-based programs, provided strategies address the contextual and structural barriers that impede their success. Group concept mapping helped distill and prioritize initial leverage points for action in our project catchment area by facilitating a community-engaged process of data generation and interpretation.

SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification.

Existing deep learning-based surrogate models facilitate efficient data generation, but fall short in uncertainty quantification, efficient parameter space exploration, and reverse prediction. In our work, we introduce SurroFlow, a novel normalizing flow-based surrogate model, to learn the invertible transformation between simulation parameters and simulation outputs. The model not only allows accurate predictions of simulation outcomes for a given simulation parameter but also supports uncertainty quantification in the data generation process. Additionally, it enables efficient simulation parameter recommendation and exploration. We integrate SurroFlow and a genetic algorithm as the backend of a visual interface to support effective user-guided ensemble simulation exploration and visualization. Our framework significantly reduces the computational costs while enhancing the reliability and exploration capabilities of scientific surrogate models.

Gwforge: a user-friendly package to generate gravitational-wave mock data

Abstract Next-generation gravitational-wave detectors, with their improved sensitivity and wider frequency bandwidth, will be capable of observing almost every compact binary coalescence signal from epochs before the first stars began to form, increasing the number of detectable binaries to hundreds of thousands annually. This will enable us to observe compact objects through cosmic time, probe extreme matter phenomena, do precision cosmology, study gravity in strong field dynamical regimes and potentially allow observation of fundamental physics beyond the standard model. However, the richer data sets produced by these detectors will pose new computational, physical and astrophysical challenges, necessitating the development of novel algorithms and data analysis strategies. To aid in these efforts, this paper introduces gwforge, a user-friendly, lightweight Python package, to generate mock data for next-generation detectors. gwforge allows users to seamlessly simulate data while abstracting away technical complexities, enabling more efficient testing and development of analysis pipelines. Additionally, the package’s data generation process is optimized using high-throughput systems like HTCondor, significantly speeding up the simulation of large populations of gravitational-wave events. We demonstrate the package’s capabilities through data simulation examples and highlight a few potential applications: performance loss due to foreground noise, bright-siren cosmology and impact of waveform systematics on binary parameter estimation.

BEEx Is an Open-Source Tool That Evaluates Batch Effects in Medical Images to Enable Multicenter Studies.

The batch effect is a nonbiological variation that arises from technical differences across different batches of data during the data generation process for acquisition-related reasons, such as collection of images at different sites or using different scanners. This phenomenon can affect the robustness and generalizability of computational pathology- or radiology-based cancer diagnostic models, especially in multicenter studies. To address this issue, we developed an open-source platform, Batch Effect Explorer (BEEx), that is designed to qualitatively and quantitatively determine whether batch effects exist among medical image datasets from different sites. A suite of tools was incorporated into BEEx that provide visualization and quantitative metrics based on intensity, gradient, and texture features to allow users to determine whether there are any image variables or combinations of variables that can distinguish datasets from different sites in an unsupervised manner. BEEx was designed to support various medical imaging techniques, including microscopy and radiology. Four use cases clearly demonstrated the ability of BEEx to identify batch effects and validated the effectiveness of rectification methods for batch effect reduction. Overall, BEEx is a scalable and versatile framework designed to read, process, and analyze a wide range of medical images to facilitate the identification and mitigation of batch effects, which can enhance the reliability and validity of image-based studies. Significance: BEEx is a prescreening tool for image-based analyses that allows researchers to evaluate batch effects in multicenter studies and determine their origin and magnitude to facilitate development of accurate AI-based cancer models.

Short-term forecasting of solar irradiance using decision tree-based models and non-parametric quantile regression.

The renewable energy industry requires accurate forecasts of intermittent solar irradiance (SI) to effectively manage solar power generation and supply. Introducing the random forests (RFs) model and its hybridisation with quantile regression modelling, the quantile regression random forest (QRRF), can help improve the forecasts' accuracy. This paper assesses the RFs and QRRF models against the quantile generalised additive model (QGAM) by evaluating their forecast performances. A simulation study of multivariate data-generating processes was carried out to compare the forecasting accuracy of the models when predicting global horizontal solar irradiance. The QRRF and QGAM are completely new forecasting frameworks for SI studies, to the best of our knowledge. Simulation results suggested that the introduced QRRF compared well with the QGAM when predicting the forecast distribution. However, the evaluations of the pinball loss scores and mean absolute scaled errors demonstrated a clear superiority of the QGAM. Similar results were obtained in an application to real-life data. Therefore, we recommend that the QGAM be preferred ahead of decision tree-based models when predicting solar irradiance. However, the QRRF model can be used alternatively to predict the forecast distribution. Both the QGAM and QRRF modelling frameworks went beyond representing forecast uncertainty of SI as probability distributions around a prediction interval to give complete information through the estimation of quantiles. Most SI studies conducted are residual and/or non-parametric modelling that are limited to represent information about the conditional mean distribution. Extensions of the QRRF and QGAM frameworks can be made to model other renewable sources of energy that have meteorological characteristics similar to solar irradiance.

Bayesian Nonparametric Sequential Search

Sequential search models are popular in marketing for studying consumer search behavior. Current search models use parametric assumptions regarding different aspects of the models, such as random shocks, search costs, and consumer preferences. These assumptions can be restrictive. The authors develop a novel Bayesian nonparametric framework for sequential search to flexibly model unknown distributions. They also develop Markov chain Monte Carlo methods for inferring the model unknowns. The authors conduct simulation studies to demonstrate that currently popular parametric search models can yield incorrect estimates and inferences when the data-generating process deviates from their assumptions. In contrast, the proposed model accurately recovers these quantities for various data-generating processes. The methodology is then applied to online search and purchase data from a Japanese retailer. The results show that several heterogeneity distributions have complex patterns, such as multimodality and skewness, that are not captured by a parametric benchmark model. The authors also estimate the monetary value of search costs, the price elasticities, and a counterfactual profit gain under a personalized couponing strategy and find substantial differences in the results from the two models.

Studying economic complexity with agent-based models: advances, challenges and future perspectives

AbstractAgent-based computational economics has considerable achievements. However, it has gone too quickly into a direction similar to the one of models based on solely analytical—as opposed to algorithmic—dynamic systems of difference equations. An increasingly large focus has been put on matching moments of real-world time series of data, a set of stylised facts, or on estimation. Reasons why this is not desirable are discussed. Firstly, both estimation and inference from models will be biased, unless they represent the real data-generating processes. Secondly, surrendering the attempt to incorporate realistic microfoundations is not only against the original ACE agenda, but also is subject to a form of Lucas critique. Thirdly, characteristics of complex systems, especially differences between feedback loops and emergent phenomena that characterise systems of various levels of complexity, undermine the justification of building structurally simplistic models. That is, an attempt at reducing the interaction of many different sectors, populated with agents using various decision rules will yield information loss, i.e., some phenomena by definition are possible to emerge only in systems of higher levels of complexity. A different research agenda is proposed, with the aim of systematically analysing and uncovering the mechanisms, feedback loops and impact channels of complex multi-sectoral economic and financial systems.

Combining density forecast accuracy tests: an application to agricultural, energy, and metal commodities

Abstract This study develops a new methodology for combining density forecast accuracy tests and assessing the relevance of psychological indicators in predicting commodity returns. Density forecasts provide a complete description of the uncertainty associated with a prediction and are highly requested by policymakers, central bankers, and financial operators to define policy actions, manage financial risks, and assess portfolio selection. The proposed methodology combines different tests and derives the p-value of the resulting test statistic by Monte Carlo simulations. To assess the power of the proposed methodology, we implement a set of experiments for several data-generating processes. Based on an empirical forecasting exercise applied to agricultural, energy, and metal commodities, we find that sentiment variables and psychological factors improve the density forecasts of commodity futures returns, especially for agricultural commodities. Additionally, combinations of sentiment variables are more powerful in predicting returns than considering them separately.

EXPRESS: Identifying Competitors in Geographical Markets Using the CSIS Method

Identifying the most relevant competitors in a geographical market is crucial for businesses with a significant offline presence, such as hotels, restaurants, and retail stores. The specific location of a business and the geographical density of potential competitors are critical factors in determining the competitive structure. However, this task can be challenging when the potential number of competitors is large and the competition is asymmetric. In this study, we apply the Conditional Sure Independence Screening (CSIS) method to a system of demand functions for competitor identification. This method offers significant computational efficiency by estimating a marginal regression for each potential competitor, rather than a full model consisting of all potential competitors. To validate the effectiveness of the CSIS method and explore the boundary conditions of its performance, we conduct extensive simulation analyses under different spatial data-generating processes. Our findings demonstrate that the CSIS method outperforms multiple other variable selection methods and remains robust under spatial misspecifications. Then we apply the CSIS method to hotel competition in two U.S. geographical regions, illustrating how the competitive structure varies across geographical densities and market segments. Finally, we highlight how managers can strategically use the results and outline the potential of the method for other non-geographical applications.

The relative data hungriness of unpenalized and penalized logistic regression and ensemble-based machine learning methods: the case of calibration.

Machine learning methods are increasingly being used to predict clinical outcomes. Optimism is the difference in model performance between derivation and validation samples. The term "data hungriness" refers to the sample size needed for a modelling technique to generate a prediction model with minimal optimism. Our objective was to compare the relative data hungriness of different statistical and machine learning methods when assessed using calibration. We used Monte Carlo simulations to assess the effect of number of events per variable (EPV) on the optimism of six learning methods when assessing model calibration: unpenalized logistic regression, ridge regression, lasso regression, bagged classification trees, random forests, and stochastic gradient boosting machines using trees as the base learners. We performed simulations in two large cardiovascular datasets each of which comprised an independent derivation and validation sample: patients hospitalized with acute myocardial infarction and patients hospitalized with heart failure. We used six data-generating processes, each based on one of the six learning methods. We allowed the sample sizes to be such that the number of EPV ranged from 10 to 200 in increments of 10. We applied six prediction methods in each of the simulated derivation samples and evaluated calibration in the simulated validation samples using the integrated calibration index, the calibration intercept, and the calibration slope. We also examined Nagelkerke's R2, the scaled Brier score, and the c-statistic. Across all 12 scenarios (2 diseases × 6 data-generating processes), penalized logistic regression displayed very low optimism even when the number of EPV was very low. Random forests and bagged trees tended to be the most data hungry and displayed the greatest optimism. When assessed using calibration, penalized logistic regression was substantially less data hungry than methods from the machine learning literature.

Traffic Flow Outlier Detection for Smart Mobility Using Gaussian Process Regression Assisted Stochastic Differential Equations

Current methods for detecting outliers in traffic streaming data often struggle to capture real-time dynamic changes in traffic conditions and differentiate between genuine changes and anomalies. This study proposes a novel approach to outlier detection in traffic streaming data that effectively addresses stochasticity and uncertainty in observations. The proposed method utilizes Stochastic Differential Equations (SDEs) and Gaussian Process Regression (GPR). By employing SDEs, we can capture drift and diffusion estimates in traffic streaming data, providing a more comprehensive modeling of the data generation process. Integrating GPR allows precise Bayesian posterior inferences for outlier detection within the SDE framework. To improve practicality, we introduce a flexible threshold-setting mechanism using statistical testing to control the false positive rate. This adaptability helps strike a balance between model fitting and complexity in outlier detection. Compared to traditional SDE-based methods, our SDE-GPR outlier detection method demonstrates enhanced robustness and better adaptability to the complexities of traffic systems. This is evidenced through an empirical study using time series data collected in California, USA. Overall, this study introduces a more advanced and accurate approach to outlier detection in traffic streaming data, paving the way for improved real-time traffic condition monitoring and management.

Score-driven exponential random graphs: A new class of time-varying parameter models for temporal networks.

Motivated by the increasing abundance of data describing real-world networks that exhibit dynamical features, we propose an extension of the exponential random graph models (ERGMs) that accommodates the time variation of its parameters. Inspired by the fast-growing literature on dynamic conditional score models, each parameter evolves according to an updating rule driven by the score of the ERGM distribution. We demonstrate the flexibility of score-driven ERGMs (SD-ERGMs) as data-generating processes and filters and show the advantages of the dynamic version over the static one. We discuss two applications to temporal networks from financial and political systems. First, we consider the prediction of future links in the Italian interbank credit network. Second, we show that the SD-ERGM allows discriminating between static or time-varying parameters when used to model the U.S. Congress co-voting network dynamics.

Climate change and economic growth: Some critical reflections

Global warming may affect the economic development and, thus, the welfare of people around the world. Therefore, the economic effects of a changing climate should be known in order to be able to design appropriate policy responses. In the economics literature, one research field empirically analyzes the growth effects of global warming. But often those studies do not account for economic variables that have turned out to be significant in explaining economic growth. In addition, they frequently fail to check for the robustness of their outcomes. This can give rise to biased results regarding the growth process and, therefore, does not necessarily reflect the true data-generating process. Hence, the question comes up: how valid and reliable the results are. Therefore, economic analyses should be undertaken that study the robustness of the results as regards the integration of fundamental economic variables. When policy recommendations are made on how to deal with global warming, we argue that they should be based on robust results only. If that does not hold, economic policy risks being inadequate, giving rise to substantial welfare losses.

A bayesian shared component spatial modeling approach for identifying the geographic pattern of local associations: a case study of young offenders and violent crimes in Greater Toronto Area

Background settingTraditional spatial or non-spatial regression techniques require individual variables to be defined as dependent and independent variables, often assuming a unidirectional and (global) linear relationship between the variables under study. This research studies the Bayesian shared component spatial (BSCS) modeling as an alternative approach to identifying local associations between two or more variables and their spatial patterns.MethodsThe variables to be studied, young offenders (YO) and violent crimes (VC), are treated as (multiple) outcomes in the BSCS model. Separate non-BSCS models that treat YO as the outcome variable and VC as the independent variable have also been developed. Results are compared in terms of model fit, risk estimates, and identification of hotspot areas.ResultsCompared to the traditional non-BSCS models, the BSCS models fitted the data better and identified a strong spatial association between YO and VC. Using the BSCS technique allowed both the YO and VC to be modeled as outcome variables, assuming common data-generating processes that are influenced by a set of socioeconomic covariates. The BSCS technique offered smooth and easy mapping of the identified association, with the maps displaying the common (shared) and separate (individual) hotspots of YO and VC.ConclusionsThe proposed method can transform existing association analyses from methods requiring inputs as dependent and independent variables to outcome variables only and shift the reliance on regression coefficients to probability risk maps for characterizing (local) associations between the outcomes.

Efficient approximate Bayesian inference for quantifying uncertainty in multiscale animal movement models

It is becoming increasingly important for wildlife managers and conservation ecologists to understand which resources are selected or avoided by an animal and how to best predict future spatial distributions of animal populations in the long term. However, inferring the patterns of space use by animals is a challenging multiscale inference problem, and formal uncertainty quantification of parameter estimates is an essential component of models that provide useful predictions across scales. In this study, we develop an approximate Bayesian inference framework for step selection models of animal movement which quantifies the uncertainty in estimates of resource selection and avoidance parameters within the Bayesian paradigm. The framework allows joint inference of movement and resource selection parameters of animals and is multiscale in that parameters inferred from fine scale movement steps scale to produce predictions of long-term patterns of space use. Our analysis focuses on simulated movement data in which we test the performance of our framework by altering movement parameters in the data-generating process. In our simulations, individuals respond to two environmental covariates and we employ all combinations of positive and negative selection coefficients corresponding to attraction to an environmental feature and avoidance of an environmental feature, respectively. In all scenarios, we recover the movement parameters used for the simulation of synthetic movement data using variational inference, an approximate Bayesian method, allowing us to formally quantify the uncertainty associated with each parameter for varying data set sizes. Our framework successfully recovered all combinations of movement parameters of the simulated data and accurately captured their posterior distributions given the available data suggesting that the framework is reliable and suitable for inferring how animals select resources and move on a landscape.Notably, our analysis shows that even for reasonably large data sets (circa 10,000 observations) there can still be considerable uncertainty associated with resource selection parameters which can in turn lead to inaccurate predictions of long term space use if not properly incorporated into the modelling approach. To further illustrate the utility of our approach, we also present a case study of its application to an example data set consisting of GPS locations of a fisher (Martes pennanti). Our approach will be of interest to ecologists looking to address conservation questions such as when and where animals are likely to spend most of their time. Furthermore, the approach could be used to predict new suitable areas for conservation based on how GPS collared animals use or avoid resources while including uncertainty around the predictions, thereby helping to make informed management decisions.

A Cross-Sectional Study of Serum Levels of Several Vitamins in Healthy and Unhealthy Periodontal Conditions

Vitamins are examples of micronutrients, which are nutrients needed in very minute quantities. They're crucial to metabolism and keeping tissues working properly. The purpose of this study was to determine whether vitamin deficiencies are associated with an increased risk of periodontitis. There was a total of 110 participants: 55 with chronic periodontitis spread throughout the mouth and 55 periodontally healthy individuals. Clinical parameters that were assessed included: Clinical attachment loss (in millimetres) and gingival index (in millimetres). Vitamin B12, vitamin D, folate, as well as vitamin E levels in serum samples were measured. Also measured were Beta-carotene and Beta-cryptoxanthin levels. The acquired data was aggregated and analysed using statistical programmes. All periodontal status clinical measures in the periodontitis group were statistically significantly different from those in the healthy volunteers group (p < 0.0001). Vitamin A precursors Beta-carotene and Beta-cryptoxanthin, folate, D, vitamins B12, and E, and vitamin E were all significantly lower in the periodontitis group compared to the healthy volunteers, but the difference was only statistically significant for vitamins B12 as well as D (p < 0.05). Finally, we conclude that serum micronutrient levels, particularly those of Vitamin A, Vitamin B12, and Vitamin D, may be modifiable risk factors for periodontal disease. In addition to practicing good dental hygiene on a regular basis, eating a diet rich in the right kinds of vitamins may play a significant part in warding off periodontitis.

A Time Series Synthetic Control Causal Evaluation of the UK’s Mini-Budget Policy on Stock Market

In this paper, we propose a modified synthetic control causal analysis for time series data with volatility in terms of absolute value of return outcomes taken into account in constructing the prediction of potential outcomes for time series causal analysis. The consistency property of the synthetic weight parameter estimators is developed theoretically under a time series data-generating process framework. The application to evaluate the UK’s mini-budget policy, announced by the then Chancellor on 23 September 2022, which had significant implications for the stock market, is examined and analysed. Comparisons with traditional synthetic control and synthetic difference in difference (DID) methods for evaluation of the effect of the mini-budget policy on the UK’s stock market are also discussed.

An analysis of the use of historical control data in the assessment of regulatory pesticide toxicity studies

The concurrent control group is the most important reference for the interpretation of toxicity studies. However, pooled information on control animals from independent studies, i.e., historical control data (HCD), is also used for the interpretation of results. Currently, an overview on actual HCD use in regulatory toxicology is lacking. Therefore, we evaluated the HCD use of the Joint FAO/WHO Meeting on Pesticide Residues from 2004 to 2021 and compared it with recommendations in regulatory guidelines and in the literature. We found that HCD was used routinely and exclusively to avoid potential false positive decisions regarding the treatment-relatedness of effects, mostly using the HCD range, i.e., the most extreme values, as a benchmark. HCD were not used to avoid potential false negative decisions or for quality control of the index study. The central assumption of the HCD use, namely that the HCD and control group of the index study follow the same underlying distribution because they are samples of the same data generation process, was not investigated, although numerous factors potentially contribute to effect variation between the different control groups pooled in the HCD. We recommend that the existing guidelines be revised to improve the robustness and transparency of toxicological assessments.

Modeling extreme events: Univariate and multivariate data-driven approaches

AbstractThis article summarizes the contribution of team genEVA to the EVA (2023) Conference Data Challenge. The challenge comprises four individual tasks, with two focused on univariate extremes and two related to multivariate extremes. In the first univariate assignment, we estimate a conditional extremal quantile using a quantile regression approach with neural networks. For the second, we develop a fine-tuning procedure for improved extremal quantile estimation with a given conservative loss function. In the first multivariate sub-challenge, we approximate the data-generating process with a copula model. In the remaining task, we use clustering to separate a high-dimensional problem into approximately independent components. Overall, competitive results were achieved for all challenges, and our approaches for the univariate tasks yielded the most accurate quantile estimates in the competition.

Bias Mitigation via Synthetic Data Generation: A Review

Artificial intelligence (AI) is widely used in healthcare applications to perform various tasks. Although these models have great potential to improve the healthcare system, they have also raised significant ethical concerns, including biases that increase the risk of health disparities in medical applications. The under-representation of a specific group can lead to bias in the datasets that are being replicated in the AI models. These disadvantaged groups are disproportionately affected by bias because they may have less accurate algorithmic forecasts or underestimate the need for treatment. One solution to eliminate bias is to use synthetic samples or artificially generated data to balance datasets. Therefore, the purpose of this study is to review and evaluate how synthetic data can be generated and used to mitigate biases, specifically focusing on the medical domain. We explored high-quality peer-reviewed articles that were focused on synthetic data generation to eliminate bias. These studies were selected based on our defined inclusion criteria and exclusion criteria and the quality of the content. The findings reveal that generated synthetic data can help improve accuracy, precision, and fairness. However, the effectiveness of synthetic data is closely dependent on the quality of the data generation process and the initial datasets used. The study also highlights the need for continuous improvement in synthetic data generation techniques and the importance of evaluation metrics for fairness in AI models.