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PurposeReal world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology.MethodsWe performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology.ResultsArtificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping.ConclusionsDespite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.

HighlightsWe illustrate the steps involved in carrying out cost-effectiveness analysis using net benefit regressions with possibly censored demo data by providing step-by-step guidance and code applied to a data set.We demonstrate the importance of these new methods by illustrating how naïve methods for handling censoring can lead to biased cost-effectiveness results.

AbstractMany recent articles in public health risk assessment have stated that causal conclusions drawn from observational data must rely on inherently untestable assumptions. They claim that such assumptions ultimately can only be evaluated by informed human judgments. We call this the subjective approach to causal interpretation of observational results. Its theoretical and conceptual foundation is a potential outcomes model of causation in which counterfactual outcomes cannot be observed. It risks depriving decision-makers and the public of the key benefits of traditional objective science, which invites scrutiny and independent verification through testable causal models and interventional hypotheses. We introduce an alternative objective approach to causal analysis of exposure-response relationships in observational data. This is designed to be more objective in the specific sense that it is independently verifiable (or refutable) and data-driven, requiring no inherently untestable assumptions. This approach uses empirically testable interventional causal models, specifically causal Bayesian networks (CBNs), instead of untestable potential outcomes models. It enables empirical validation of causal claims through Invariant Causal Prediction (ICP) tests across multiple studies. We explain how to use CBNs and individual conditional expectation (ICE) plots to quantify the effects on health risks of changing exposures while taking into account realistic complexities such as imperfectly controlled confounding, missing data, and measurement error. By ensuring that all causal assumptions are explicit and empirically testable, our framework may help to improve the reliability and transparency of causal inferences in health risk assessments. [ABSTRACT FROM AUTHOR]

Background: Recent developments in causal inference and machine learning (ML) allow for the estimation of individualized treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients' observed covariates. ITEs can be used to stratify health policy decisions according to individual characteristics and potentially achieve greater population health. Little is known about the appropriateness of available ML methods for use in health technology assessment. Methods: In this scoping review, we evaluate ML methods available for estimating ITEs, aiming to help practitioners assess their suitability in health technology assessment. We present a taxonomy of ML approaches, categorized by key challenges in health technology assessment using observational data, including handling time-varying confounding and time-to event data and quantifying uncertainty. Results: We found a wide range of algorithms for simpler settings with baseline confounding and continuous or binary outcomes. Not many ML algorithms can handle time-varying or unobserved confounding, and at the time of writing, no ML algorithm was capable of estimating ITEs for time-to-event outcomes while accounting for time-varying confounding. Many of the ML algorithms that estimate ITEs in longitudinal settings do not formally quantify uncertainty around the point estimates. Limitations: This scoping review may not cover all relevant ML methods and algorithms as they are continuously evolving. Conclusions: Existing ML methods available for ITE estimation are limited in handling important challenges posed by observational data when used for cost-effectiveness analysis, such as time-to-event outcomes, time-varying and hidden confounding, or the need to estimate sampling uncertainty around the estimates. Implications: ML methods are promising but need further development before they can be used to estimate ITEs for health technology assessments. Highlights: Estimating individualized treatment effects (ITEs) using observational data and machine learning (ML) can support personalized treatment advice and help deliver more customized information on the effectiveness and cost-effectiveness of health technologies. ML methods for ITE estimation are mostly designed for handling confounding at baseline but not time-varying or unobserved confounding. The few models that account for time-varying confounding are designed for continuous or binary outcomes, not time-to-event outcomes. Not all ML methods for estimating ITEs can quantify the uncertainty of their predictions. Future work on developing ML that addresses the concerns summarized in this review is needed before these methods can be widely used in clinical and health technology assessment–like decision making. [ABSTRACT FROM AUTHOR]

We estimate how much the average grade of students enrolled in introductory macroeconomics changes when the professor sends a reminder email. We utilize observational data to implement a difference-in-differences strategy and identify the plausibly causal effect of email nudges on the student's grade. The professor's reminder email boosts low-performing student grades by 22–28 points per 50 points. [ABSTRACT FROM AUTHOR]

Reliable and valid assessment of parenting and child behaviors is critical for clinicians and researchers alike, and observational measures of parenting behaviors are often considered the gold standard for assessing parenting and parent–child interaction quality. The present study sought to evaluate the reliability and validity of the Coder Impressions Questionnaire–Kindergarten (COIMP-K) measure. The present study was a secondary analysis of 274 parents and their children participating in a randomized control trial testing a brief parenting intervention for parents of children entering kindergarten. Families participated in baseline and follow-up assessments and videotaped observational tasks. Graduate and undergraduate coders completed the COIMP-K after achieving reliability. The aims of the present study were to assess COIMP-K's (a) internal consistency using intercorrelations among COIMP-K subscales, (b) construct validity, (c) convergent validity by comparing the COIMP-K subscales to parents' self-report of similar behaviors, (d) discriminant validity by comparing subscales to a parent–teacher communication measure as it is unrelated to parenting or child behaviors, and (e) congruence across time. The authors hypothesized that the COIMP-K would demonstrate adequate internal consistency (Cortina, 1993), adequate construct, convergent, and discriminant validity and find congruence of the measure across time. The results demonstrated that the factors had adequate internal consistency, construct, convergent, and discriminant validity, as well as longitudinal replicability and congruence over time. The study demonstrates that the COIMP-K is a reliable and valid tool for assessing observed family behaviors. [ABSTRACT FROM AUTHOR]

This study aims to assess the ability of the Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model version 4.7.1 to reproduce historical monthly mean temperatures and precipitation in different physical geographical conditions of Georgia. RegCM4.7.1 simulation has been done at 12 km resolution over the territory of Georgia from 1985 to 2014, with the hourly ERA5 high-resolution atmospheric reanalysis of the global climate data of the European Centre for Medium-Range Weather Forecasts (ECMWF) as boundary conditions. Standard deviation is used to evaluate the model's performance against Georgia's meteorological station data. This metric helps quantify the agreement between model outputs and observed data. Conducting historical runs and validating the model against observed data contributes to understanding how regional climate models perform in regions with diverse geographical features and is crucial for ensuring the reliability of future climate projections. A group of weather stations with the best data modeling results in all months of the year when the difference between the actual and model data does not exceed the standard deviation value is Kutaisi, Gori, Sagarejo, Tbilisi, and Tsalka for air temperature and Akhalkalaki, Akhaltsikhe, Dedoplistskaro, Gori, Sagarejo, Tbilisi, Telavi, and Tianeti for precipitation. The modeling results are generally satisfactory, indicating that the model can be used effectively for future climate projections in Georgia. These findings provide valuable insights for policymakers, stakeholders, and researchers working on climate change adaptation in Georgia. [ABSTRACT FROM AUTHOR]

A main research goal in various studies is to use an observational data set and provide a new set of counterfactual guidelines that can yield causal improvements. Dynamic Treatment Regimes (DTRs) are widely studied to formalize this process and enable researchers to find guidelines that are both personalized and dynamic. However, available methods in finding optimal DTRs often rely on assumptions that are violated in real-world applications (e.g., medical decision making or public policy), especially when (a) the existence of unobserved confounders cannot be ignored, and (b) the unobserved confounders are time varying (e.g., affected by previous actions). When such assumptions are violated, one often faces ambiguity regarding the underlying causal model that is needed to be assumed to obtain an optimal DTR. This ambiguity is inevitable because the dynamics of unobserved confounders and their causal impact on the observed part of the data cannot be understood from the observed data. Motivated by a case study of finding superior treatment regimes for patients who underwent transplantation in our partner hospital (Mayo Clinic) and faced a medical condition known as new-onset diabetes after transplantation, we extend DTRs to a new class termed Ambiguous Dynamic Treatment Regimes (ADTRs), in which the causal impact of treatment regimes is evaluated based on a "cloud" of potential causal models. We then connect ADTRs to Ambiguous Partially Observable Markov Decision Processes (APOMDPs) proposed by Saghafian (2018), and consider unobserved confounders as latent variables but with ambiguous dynamics and causal effects on observed variables. Using this connection, we develop two reinforcement learning methods termed Direct Augmented V-Learning (DAV-Learning) and Safe Augmented V-Learning (SAV-Learning), which enable using the observed data to effectively learn an optimal treatment regime. We establish theoretical results for these learning methods, including (weak) consistency and asymptotic normality. We further evaluate the performance of these learning methods both in our case study (using clinical data) and in simulation experiments (using synthetic data). We find promising results for our proposed approaches, showing that they perform well even compared with an imaginary oracle who knows both the true causal model (of the data-generating process) and the optimal regime under that model. Finally, we highlight that our approach enables a two-way personalization; obtained treatment regimes can be personalized based on both patients' characteristics and physicians' preferences. This paper was accepted by David Simchi-Levi, data science. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.00883. [ABSTRACT FROM AUTHOR]

Abstract Background A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement. Methods Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55–84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors. Results BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration. Conclusions We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia.

Due to their local, sudden nature, which complicates forecasts, squalls often cause significant material damage and are classified as extreme weather events. Diagnosing squalls and increasing the accuracy of modeling to improve their operational forecasting is very relevant. Determining the main characteristics of squalls by diagnosing particular cases and analyzing such cases is of great interest in order to establish causes of extreme events, which are still insufficiently studied. The source of obtaining primary information on the characteristics of squalls is the processing of a large array of observational data from a high-altitude meteorological mast (HMM) of the Institute of Experimental Meteorology of the Federal State Budgetary Institution Research and Production Association Typhoon, presented in this article for 2014–2023. Such experimental data on a number of long-term observations on the HMM has made it possible, after processing and analyzing the velocity fields, to fix intense squalls passing through the sector. The main characteristics of these special cases of squalls are obtained and analyzed, some of which are simultaneously simulated using the WRF-ARW nonhydrostatic mesoscale atmospheric model, adapted for the observation area; verification is also carried out with data from field experiments. A good agreement between the results and observational data for the particular case of squalls is shown. [ABSTRACT FROM AUTHOR]

In this paper, we employ parametrization techniques within the framework of f (T) gravity to investigate the deceleration parameter (DP), a key quantity characterizing the universe's expansion dynamics. By analyzing the DP, we gain valuable insights into the nature of cosmic constituents and their impact on the universe's evolution. We utilize a combination of observational data, including 31 Cosmic Chronometers (CC) measurements, 1048 Type Ia Supernovae (SNIa), 17 Baryon Acoustic Oscillation (BAO) measurements, Cosmic Microwave Background (CMB), 162 Gamma Ray Bursts (GRB), and 24 observations of compact radio quasars (Q). We employ the Markov chain Monte Carlo (MCMC) sampling technique to estimate the best-fit range of model parameters. Cosmological and cosmographic parameters are investigated, and their implications in the context of cosmology have been discussed. We also analyze the statefinder and Om diagnostic to gain deeper insights into the universe's behavior. Furthermore, we conduct statistical analyses to compare our model with the standard Λ CDM model. Our investigation also includes the study of physical parameters, providing comprehensive insights into the cosmological behavior within the f (T) gravity framework. The results and comparisons presented in this work contribute to a deeper understanding of the universe's dynamics and provide valuable implications for the cosmological model under consideration. [ABSTRACT FROM AUTHOR]

Purpose: We investigated time trends in validation performance characteristics for six sources of death data available within the Healthcare Integrated Research Database (HIRD) over 8 years. Methods: We conducted a secondary analysis of a cohort of advanced cancer patients with linked National Death Index (NDI) data identified in the HIRD between 2010 and 2018. We calculated sensitivity, specificity, positive predictive value, and negative predictive value for six sources of death status data and an algorithm combining data from available sources using NDI data as the reference standard. Measures were calculated for each year of the study including all members in the cohort for at least 1 day in that year. Results: We identified 27 396 deaths from any source among 40 692 cohort members. Between 2010 and 2018, the sensitivity of the Death Master File (DMF) decreased from 0.77 (95% CI = 0.76, 0.79) to 0.12 (95% CI = 0.11, 0.14). In contrast, the sensitivity of online obituary data increased from 0.43 (95% CI = 0.41, 0.45) in 2012 to 0.71 (95% CI = 0.68, 0.73) in 2018. The sensitivity of the composite algorithm remained above 0.83 throughout the study period. PPV was observed to be high from 2010 to 2016 and decrease thereafter for all sources. Specificity and NPV remained at high levels throughout the study. Conclusions: We observed that the sensitivity of mortality data sources compared with the NDI could change substantially between 2010 and 2018. Other validation characteristics were less variable. Combining multiple sources of mortality data may be necessary to achieve adequate performance particularly for multiyear studies. [ABSTRACT FROM AUTHOR]

To study the roles that different nodes play in differentiating Bayesian networks under two states, such as control versus disease, we formulate two node‐specific scores to facilitate such assessment. The first score is motivated by the prediction invariance property of a causal model. The second score results from modifying an existing score constructed for differential analysis of undirected networks. We develop strategies based on these scores to identify nodes responsible for topological differences between two Bayesian networks. Synthetic data and real‐life data from designed experiments are used to demonstrate the efficacy of the proposed methods in detecting responsible nodes. [ABSTRACT FROM AUTHOR]

Background: A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement. Methods: Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55–84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors. Results: BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration. Conclusions: We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia. [ABSTRACT FROM AUTHOR]

This paper offers an accessible discussion of graphical causal models and how such a framework can be used to help identify causal relations. A graphical causal model represents a researcher's qualitative assumptions. As a result of the credibility revolution, there is growing interest to properly estimate cause-and-effect relationships. Using several examples, we illustrate how graphical models can and cannot be used to identify causation from observational data. Further, we offer a replication of a previous study that explored college enrolment by high school seniors who were eligible for student aid. From the original study, we use a graphical causal model to motivate the quantitative and qualitative modelling assumptions. Using a similar difference-in-difference approach based on propensity score matching, we estimate a smaller average treatment effect than the original study. The smaller estimated effect arguably stems from the graphical causal model's delineation of the original model specification. [ABSTRACT FROM AUTHOR]

The use of structural equation models for causal inference from panel data is critiqued in the causal inference literature for unnecessarily relying on a large number of parametric assumptions, and alternative methods originating from the potential outcomes framework have been recommended, such as inverse probability weighting (IPW) estimation of marginal structural models (MSMs). To better understand this criticism, we describe three phases of causal research. We explain (differences in) the assumptions that are made throughout these phases for structural equation modeling (SEM) and IPW-MSM approaches using an empirical example. Second, using simulations we compare the finite sample performance of SEM and IPW-MSM for the estimation of time-varying exposure effects on an end-of-study outcome under violations of parametric assumptions. Although increased reliance on parametric assumptions does not always translate to increased bias (even under model misspecification), researchers are still well-advised to acquaint themselves with causal methods from the potential outcomes framework. [ABSTRACT FROM AUTHOR]

Regression on observational data can fail to capture a causal relationship in the presence of unobserved confounding. Confounding strength measures this mismatch, but estimating it requires itself additional assumptions. A common assumption is the independence of causal mechanisms, which relies on concentration phenomena in high dimensions. While high dimensions enable the estimation of confounding strength, they also necessitate adapted estimators. In this paper, we derive the asymptotic behavior of the confounding strength estimator by Janzing and Schölkopf (2018) and show that it is generally not consistent. We then use tools from random matrix theory to derive an adapted, consistent estimator. [ABSTRACT FROM AUTHOR]

The authors have examined a Friedmann Robertson Walker cosmological model in Hořava‐Lifshitz gravity by using a dynamical system approach. A set of autonomous equations is derived and their solutions are calculated. The critical points from these equations and find the characteristics values with the analysis of the physical interpretation of the phase space for this system are assessed. Three stable critical points are found and the values of the physical parameters and the scale factor's expressions at each critical points are displayed in Tables 1, 2, and 3. A hybrid scale factor to develop the model, which results in a phase transition from deceleration to acceleration is used. The suitable values of the parameters are governed by applying the Monte Chain Monte Carlo method technique to the Hubble 46 and joint Hubble 46 and Baryon Acoustic Oscillations 15 datasets. In contrast to the negative behavior of pressure, the positive behavior of energy density and q=−1$q=-1$ illustrate the Universe's acceleration epoch and the ΛCDM$\Lambda{\rm CDM}$ model is represented by the EoS parameter ω=−1$\omega =-1$. The authors investigated that the energy conditions and their model violates the strong energy condition. Utilizing Om$Om$ diagnostic test, it is found that the model represents phantom behavior. The thermodynamical perspective for the model is also examined. The model accurately explained the Universe's propagation history and fits well with contemporary cosmic data. The authors have examined a Friedmann Robertson Walker cosmological model in Hoˇrava‐Lifshitz gravity by using a dynamical system approach. A set of autonomous equations is derived and their solutions are calculated. The critical points from these equations and find the characteristics values with the analysis of the physical interpretation of the phase space for this system are assessed. Three stable critical points are found and the values of the physical parameters and the scale factor's expressions at each critical points are displayed in Tables 1, 2, and 3. A hybrid scale factor to develop the model, which results in a phase transition from deceleration to acceleration is used. The suitable values of the parameters are governed by applying the Monte Chain Monte Carlo method technique to the Hubble 46 and joint Hubble 46 and Baryon Acoustic Oscillations 15 datasets. In contrast to the negative behavior of pressure, the positive behavior of energy density and q=−1$q=-1$ illustrate the Universe's acceleration epoch and the ΛCDM$\Lambda{\rm CDM}$ model is represented by the EoS parameter ω=−1$\omega =-1$. The authors investigated that the energy conditions and their model violates the strong energy condition. Utilizing Om$Om$ diagnostic test, it is found that the model represents phantom behavior. The thermodynamical perspective for the model is also examined. The model accurately explained the Universe's propagation history and fits well with contemporary cosmic data. [ABSTRACT FROM AUTHOR]

In this paper, we propose a specialized parameterization for the Hubble parameter, inspired by Λ CDM cosmology, to investigate the cosmic expansion history of the Universe. This parameterization is employed to analyze the universe's late-time behavior within the context of Q n gravity, where Q represents non-metricity. By using data from 57 Hubble data points, 1048 supernova (SNe) data points, and six Baryon Acoustic Oscillation (BAO) data points, we determine the optimal values for the model parameters. Additionally, we explore three distinct cosmological models based on the parameter n , specifically when it takes on the values of 0. 5 5 , 1. 5 and 2. 0. The results of our analysis indicate that our proposed parameterization, along with the associated models for different values of n , predicts an accelerated cosmic expansion phase. [ABSTRACT FROM AUTHOR]

Studying causal effects of continuous treatments is important for gaining a deeper understanding of many interventions, policies, or medications, yet researchers are often left with observational studies for doing so. In the observational setting, confounding is a barrier to the estimation of causal effects. Weighting approaches seek to control for confounding by reweighting samples so that confounders are comparable across different treatment values. Yet, for continuous treatments, weighting methods are highly sensitive to model misspecification. In this article we elucidate the key property that makes weights effective in estimating causal quantities involving continuous treatments. We show that to eliminate confounding, weights should make treatment and confounders independent on the weighted scale. We develop a measure that characterizes the degree to which a set of weights induces such independence. Further, we propose a new model-free method for weight estimation by optimizing our measure. We study the theoretical properties of our measure and our weights, and prove that our weights can explicitly mitigate treatment-confounder dependence. The empirical effectiveness of our approach is demonstrated in a suite of challenging numerical experiments, where we find that our weights are quite robust and work well under a broad range of settings. for this article are available online. [ABSTRACT FROM AUTHOR]

Social work aspires to change social outcomes through policy and practice. Thus, researchers often use quantitative analysis to understand how social phenomena or policy and practice interventions change outcomes, but these causal questions cannot be answered using statistical association only. Directed acyclic graphs (DAGs), also called causal graphs, are a framework for representing assumptions about the causal relations between variables, imbuing statistical associations with causal meaning. This visual language may be more accessible than a math-based approach. We provide social work researchers with clear guidelines for using DAGs. First, we synthesize the current DAG literature and show how many statistical phenomena are represented in DAGs. Second, we describe an ordered process for building a DAG around a causal question about the impact of a treatment on an outcome. Third, we introduce a structured process called "clock and grid" for specifying the confounders that must be accounted for to estimate the unbiased causal effect. Throughout, we use working examples of social work evaluation scenarios to facilitate understanding of these concepts. We argue that DAGs represent an end-to-end conceptual framework for curating subject area knowledge that can advance social work research by informing design and analysis. [ABSTRACT FROM AUTHOR]

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OBJECTIVE: Observational studies can impact patient care but must be robust and reproducible. Nonreproducibility is primarily caused by unclear reporting of design choices and analytic procedures. This study aimed to: (1) assess how the study logic described in an observational study could be interpreted by independent researchers and (2) quantify the impact of interpretations variability on patient characteristics. MATERIALS AND METHODS: Nine teams of highly qualified researchers reproduced a cohort from a study by Albogami et al. The teams were provided the clinical codes and access to the tools to create cohort definitions such that the only variable part was their logic choices. We executed teams cohort definitions against the database and compared the number of subjects, patient overlap, and patient characteristics. RESULTS: On average, the teams interpretations fully aligned with the master implementation in 4 out of 10 inclusion criteria with at least 4 deviations per team. Cohorts size varied from one-third of the master cohort size to 10 times the cohort size (2159-63 619 subjects compared to 6196 subjects). Median agreement was 9.4% (interquartile range 15.3-16.2%). The teams cohorts significantly differed from the master implementation by at least 2 baseline characteristics, and most of the teams differed by at least 5. CONCLUSIONS: Independent research teams attempting to reproduce the study based on its free-text description alone produce different implementations that vary in the population size and composition. Sharing analytical code supported by a common data model and open-source tools allows reproducing a study unambiguously thereby preserving initial design choices.

[Objective] In the face of the new requirements and needs of the “two unified” management of natural resources, the ability to acquire natural resources intelligence, analyze and mine natural resources information, and support decision making in natural resources management is lagging behind the needs of development, and it is of great significance to build a new type of high-end think tank system in the field of natural resources for the comprehensive management of natural resources, the study of key issues, and the formulation of relevant policies.[Methods] This study summarized and analyzed the basis and characteristics of existing natural resources observation data in China as well as the current situation of the construction of think tanks in the field of natural resources in China and internationally, and described the current situation of natural resources observation data. [Results] Based on natural resources observation data and focusing on serving the needs of comprehensive natural resources management and ecological civilization construction, we proposed an overall framework of a novel high-end think tank system, consisting of data management, technical support, organizational guarantee, and application service system. [Conclusion] The construction of a new type of high-end think tank system for natural resources is a long-term work, which needs to sum up experience, gradually enrich and improve the content of the construction of the think tank system in the field of natural resources and the relevant institutional mechanism; strengthen the integration, access, supply and sharing mechanism with existing think tank platforms; establish a think tank alliance of China’s natural resources field to achieve the positive interactive situation of mutual exchange of information, mutual use of resources, mutual complementation of strengths, and mutual promotion of development, so as to achieve a positive and interactive situation of mutual promotion. It will also establish an alliance of think tanks in the field of natural resources in China, so as to achieve a benign interactive situation of mutual exchange of information, mutual use of resources, complementarity of advantages and mutual promotion of development, and to support and serve the fine, intelligent and scientific management of natural resources in the new era.

Bias in causal comparisons has a correspondence with distributional imbalance of covariates between treatment groups. Weighting strategies such as inverse propensity score weighting attempt to mitigate bias by either modeling the treatment assignment mechanism or balancing specified covariate moments. This article introduces a new weighting method, called energy balancing, which instead aims to balance weighted covariate distributions. By directly targeting distributional imbalance, the proposed weighting strategy can be flexibly utilized in a wide variety of causal analyses without the need for careful model or moment specification. Our energy balancing weights (EBW) approach has several advantages over existing weighting techniques. First, it offers a model-free and robust approach for obtaining covariate balance that does not require tuning parameters, obviating the need for modeling decisions of secondary nature to the scientific question at hand. Second, since this approach is based on a genuine measure of distributional balance, it provides a means for assessing the balance induced by a given set of weights for a given dataset. We demonstrate the effectiveness of this EBW approach in a suite of simulation experiments, and in studies on the safety of right heart catheterization and on three additional studies using electronic health record data.

Abstract Background This study compares emergency department (ED) revisits for patients receiving hospital-based substance-use support compared to those who did not receive specialized addiction services at Health Sciences North in Sudbury, Ontario, Canada. Methods The study is a retrospective observational study using administrative data from all patients presenting with substance use disorder (SUD) at Health Sciences North from January 1, 2018, and August 31, 2022 with ICD-10 codes from the Discharge Abstract Database (DAD) and the National Ambulatory Care Database (NACRS). There were two interventions under study: addiction medicine consult services (AMCS group), and specialized addiction medicine unit (AMU group). The AMCS is a consult service offered for patients in the ED and those who are admitted to the hospital. The AMU is a specialized inpatient medical unit designed to offer addiction support to stabilize patients that operates under a harm-reduction philosophy. The primary outcome was all cause ED revisit within 30 days of the index ED or hospital visit. The secondary outcome was all observed ED revisits in the study period. Kaplan–Meier curves were used to measure the proportion of 30-day revisits by exposure group. Odds ratios and Hazard Ratios were calculated using logistic regression models with random effects and Cox-proportional hazard model respectively. Results A total of 5,367 patients with 10,871 ED index visits, and 2,127 revisits between 2018 and 2022 are included in the study. 45% (2,340/5,367) of patient were not admitted to hospital. 30-day revisits were less likely among the intervention group: Addiction Medicine Consult Services (AMCS) in the ED significantly reduced the odds of revisits (OR 0.53, 95% CI 0.39–0.71, p

Prostate cancer patients who undergo prostatectomy are closely monitored for recurrence and metastasis using routine prostate-specific antigen measurements. When prostate-specific antigen levels rise, salvage therapies are recommended in order to decrease the risk of metastasis. However, due to the side effects of these therapies and to avoid over-treatment, it is important to understand which patients and when to initiate these salvage therapies. In this work, we use the University of Michigan Prostatectomy Registry Data to tackle this question. Due to the observational nature of this data, we face the challenge that prostate-specific antigen is simultaneously a time-varying confounder and an intermediate variable for salvage therapy. We define different causal salvage therapy effects defined conditionally on different specifications of the longitudinal prostate-specific antigen history. We then illustrate how these effects can be estimated using the framework of joint models for longitudinal and time-to-event data. All proposed methodology is implemented in the freely-available R package JMbayes2. [ABSTRACT FROM AUTHOR]

Introduction: People with type 2 diabetes are at heightened risk for severe outcomes related to COVID-19 infection, including hospitalization, intensive care unit admission, and mortality. This study was designed to examine the impact of premorbid use of glucagon-like peptide-1 receptor agonist (GLP-1RA) monotherapy, sodium-glucose cotransporter-2 inhibitor (SGLT-2i) monotherapy, and concomitant GLP1-RA/SGLT-2i therapy on the severity of outcomes in individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods: Utilizing observational data from the National COVID Cohort Collaborative through September 2022, we compared outcomes in 78,806 individuals with a prescription of GLP-1RA and SGLT-2i versus a prescription of dipeptidyl peptidase 4 inhibitors (DPP-4i) within 24 months of a positive SARS-CoV-2 PCR test. We also compared concomitant GLP-1RA/SGLT-2i therapy to GLP-1RA and SGLT-2i monotherapy. The primary outcome was 60-day mortality, measured from the positive test date. Secondary outcomes included emergency room (ER) visits, hospitalization, and mechanical ventilation within 14 days. Using a super learner approach and accounting for baseline characteristics, associations were quantified with odds ratios (OR) estimated with targeted maximum likelihood estimation (TMLE). Results: Use of GLP-1RA (OR 0.64, 95% confidence interval [CI] 0.56–0.72) and SGLT-2i (OR 0.62, 95% CI 0.57–0.68) were associated with lower odds of 60-day mortality compared to DPP-4i use. Additionally, the OR of ER visits and hospitalizations were similarly reduced with GLP1-RA and SGLT-2i use. Concomitant GLP-1RA/SGLT-2i use showed similar odds of 60-day mortality when compared to GLP-1RA or SGLT-2i use alone (OR 0.92, 95% CI 0.81–1.05 and OR 0.88, 95% CI 0.76–1.01, respectively). However, lower OR of all secondary outcomes were associated with concomitant GLP-1RA/SGLT-2i use when compared to SGLT-2i use alone. Conclusion: Among adults who tested positive for SARS-CoV-2, premorbid use of either GLP-1RA or SGLT-2i is associated with lower odds of mortality compared to DPP-4i. Furthermore, concomitant use of GLP-1RA and SGLT-2i is linked to lower odds of other severe COVID-19 outcomes, including ER visits, hospitalizations, and mechanical ventilation, compared to SGLT-2i use alone. Graphical abstract available for this article. [ABSTRACT FROM AUTHOR]

Objective To introduce 2 R-packages that facilitate conducting health economics research on OMOP-based data networks, aiming to standardize and improve the reproducibility, transparency, and transferability of health economic models. Materials and Methods We developed the software tools and demonstrated their utility by replicating a UK-based heart failure data analysis across 5 different international databases from Estonia, Spain, Serbia, and the United States. Results We examined treatment trajectories of 47 163 patients. The overall incremental cost-effectiveness ratio (ICER) for telemonitoring relative to standard of care was 57 472 €/QALY. Country-specific ICERs were 60 312 €/QALY in Estonia, 58 096 €/QALY in Spain, 40 372 €/QALY in Serbia, and 90 893 €/QALY in the US, which surpassed the established willingness-to-pay thresholds. Discussion Currently, the cost-effectiveness analysis lacks standard tools, is performed in ad-hoc manner, and relies heavily on published information that might not be specific for local circumstances. Published results often exhibit a narrow focus, central to a single site, and provide only partial decision criteria, limiting their generalizability and comprehensive utility. Conclusion We created 2 R-packages to pioneer cost-effectiveness analysis in OMOP CDM data networks. The first manages state definitions and database interaction, while the second focuses on Markov model learning and profile synthesis. We demonstrated their utility in a multisite heart failure study, comparing telemonitoring and standard care, finding telemonitoring not cost-effective. [ABSTRACT FROM AUTHOR]

Controlling for confounding bias is crucial in causal inference. Causal inference using data from observational studies (e.g., electronic health records) or imperfectly randomized trials (e.g., imperfect randomization or compliance) requires accounting for confounding variables. Many different methods are currently employed to mitigate bias due to confounding. This paper provides a comprehensive review and tutorial of common estimands and confounding adjustment approaches, including outcome regression, g-computation, propensity score, and doubly robust methods. We discuss bias and precision, advantages and disadvantages, and software implementation for each method. Moreover, approaches are illustrated empirically with a reproducible case study. We conclude that different scientific questions are better addressed by certain estimands. No estimand is uniformly more appropriate. Upon selecting an estimand, decisions on which estimator can be driven by performance and available background knowledge. [ABSTRACT FROM AUTHOR]

Differentiated care delivery aims to simplify care of people living with HIV, reflect their preferences, reduce burdens on the healthcare system, maintain care quality and preserve resources. However, assessing program effectiveness using observational data is difficult due to confounding by indication and randomized trials may be infeasible. Also, benefits can reach patients directly, through enrollment in the program, and indirectly, by increasing quality of and accessibility to care. Low-risk express care (LREC), the program under evaluation, is a nurse-centered model which assigns patients stable on ART to a nurse every two months and a clinician every third visit, reducing annual clinician visits by two thirds. Study population is comprised of 16,832 subjects from 15 clinics in Kenya. We focus on patient retention in care based on whether the LREC program is available at a clinic and whether the patient is enrolled in LREC. We use G-estimation to assess the effect on retention of two "strategies": (i) program availability but no enrollment; (ii) enrollment at an available program; versus no program availability. Compared to no availability, LREC results in a non-significant increase in patient retention, among patients not enrolled in the program (indirect effect), while enrollment in LREC is associated with a significant extension of the time retained in care (direct effect). G-estimation provides an analytical framework useful to the assessment of similar programs using observational data. [ABSTRACT FROM AUTHOR]

Machine learning for causal inference, particularly at the individual level, has attracted intense interest in many domains. Existing techniques focus on controlling differences in distribution between treatment groups in a data-driven manner, eliminating the effects of confounding factors. However, few of the current methods adequately discuss the difference in treatment group sizes. Two approaches, a direct and an indirect one, deal with potential missing data for estimating individual treatment with binary treatments and different treatment group sizes. We embed the two methods into certain frameworks based on the domain adaption and representation. We validate the performance of our method by two benchmarks in the causal inference community: simulated data and real-world data. Experiment results verify that our methods perform well. [ABSTRACT FROM AUTHOR]

As a fundamental parameter of the surface radiation process, land surface emissivity (LSE) is the most direct factor affecting the land surface temperature (LST) retrieval accuracy. Based on the measured data from 2017 to 2023, the characteristics of LSE changes were analysed, different timescale LSE retrieval models were developed, and the impact of LSE on pixel scale observational LST acquisition was evaluated by using unmanned aerial vehicle (UAV) remote sensing images. The results show that with changes in atmospheric environmental conditions, the LSE changes are relatively less pronounced at 3.99 µm and 4.02 µm in the mid-infrared band and at 10.8 µm and 11.8 µm in the thermal infrared band than in the other bands. At the timescale above day, the LSE is significant correlation with the normalized differential vegetation index (NDVI). Vegetation is the main parameter that dominates the LSE change under heterogeneous underlying surfaces. On a daily timescale, LSE exhibits typical diurnal variation characteristics, and the amplitude of diurnal variation is influenced mainly by shallow soil water and heat conditions. The developed LSE model based on Fengyun-4A (FY-4A) satellite remote-sensing data can effectively reproduce the daily variation characteristics of LST and avoid the 'jumping' phenomenon that may occur in the NDVI threshold method. The root-mean-square error (RMSE) between the retrieved LST and in situ data is reduced to 3.78°C, and the retrieval accuracy is better than that of the published LST product. When using UAVs to obtain pixel-scale LSTs, dry bare soil is more sensitive to LSE. When the LSE increases from 0.95 to 1.0, the average LST obtained by the UAV deviates by approximately 1.0°C. This study preliminarily provides some spatiotemporal variation patterns of LSE and lays a foundation for the later-modified LST retrieval algorithms. [ABSTRACT FROM AUTHOR]

In this work, based on the results of predictive calculations, the accuracy of reproducing the Black Sea circulation is analyzed using new approximations of nonlinear terms in the transport equations, ensuring the conservation of temperature and salinity to a power greater than two. Numerical experiments have been carried out that differ in schemes for calculating temperature and salinity. In the first experiment, traditional schemes were used to ensure the conservation of temperature and salinity in the first and second degrees; in the second one, the temperature was maintained in the first and fifth degrees and salinity in the first and third; in the third experiment, the temperature was maintained in the first and third and salinity in the first and fifth degrees. Calculations were performed on the basis of MHI model with a resolution of 1.6 km and accounting a realistic atmospheric forcing for 2016. The validation of results was carried out based on comparison of model fields with data from contact and satellite measurements of temperature and salinity in 2016. An analysis of average and root mean square errors showed that, compared to the traditional approximation, the new difference schemes for the advection–diffusion equations of heat and salt, ensuring the preservation of predictive parameters to a power greater than two, improve the accuracy of reproducing of the Black Sea salinity in the upper 100-m layer throughout the year. Root mean square errors in the salinity field decrease by 15–20%, and the upper mixed layer thickness in winter and the upper boundary depth of the thermocline layer in summer in the central part of the sea are modeled about 10% more accurately. Based on the results of three experiments, the smallest deviations from observational data were obtained when using approximations that ensure the conservation of temperature to the third degree and salinity to the fifth degree. [ABSTRACT FROM AUTHOR]

Background: This study compares emergency department (ED) revisits for patients receiving hospital-based substance-use support compared to those who did not receive specialized addiction services at Health Sciences North in Sudbury, Ontario, Canada. Methods: The study is a retrospective observational study using administrative data from all patients presenting with substance use disorder (SUD) at Health Sciences North from January 1, 2018, and August 31, 2022 with ICD-10 codes from the Discharge Abstract Database (DAD) and the National Ambulatory Care Database (NACRS). There were two interventions under study: addiction medicine consult services (AMCS group), and specialized addiction medicine unit (AMU group). The AMCS is a consult service offered for patients in the ED and those who are admitted to the hospital. The AMU is a specialized inpatient medical unit designed to offer addiction support to stabilize patients that operates under a harm-reduction philosophy. The primary outcome was all cause ED revisit within 30 days of the index ED or hospital visit. The secondary outcome was all observed ED revisits in the study period. Kaplan–Meier curves were used to measure the proportion of 30-day revisits by exposure group. Odds ratios and Hazard Ratios were calculated using logistic regression models with random effects and Cox-proportional hazard model respectively. Results: A total of 5,367 patients with 10,871 ED index visits, and 2,127 revisits between 2018 and 2022 are included in the study. 45% (2,340/5,367) of patient were not admitted to hospital. 30-day revisits were less likely among the intervention group: Addiction Medicine Consult Services (AMCS) in the ED significantly reduced the odds of revisits (OR 0.53, 95% CI 0.39–0.71, p < 0.01) and first revisits (OR 0.42, 95% CI 0.33–0.53, p < 0.01). The AMU group was associated with lower revisits odds (OR 0.80, 95% CI 0.66–0.98, p = 0.03). For every additional year of age, the odds of revisits slightly decreased (OR 0.99, 95% CI 0.98–1.00, p = 0.01) and males were found to have an increased risk compared to females (OR 1.50, 95% CI 1.35–1.67, p < 0.01). Interpretation: We observe statistically significant differences in ED revisits for patients receiving hospital-based substance-use support at Health Sciences North. Hospital-based substance-use supports could be applied to other hospitals to reduce 30-day revisits. [ABSTRACT FROM AUTHOR]

Objective 17-α-hydroxyprogesterone caproate (17-OHP) has been recommended by professional societies for the prevention of recurrent preterm birth, but subsequent clinical studies have reported conflicting efficacy results. This study aimed to contribute to the evidence base regarding the effectiveness of 17-OHP in clinical practice using real-world data. Study Design A total of 4,422 individuals meeting inclusion criteria representing recurrent spontaneous preterm birth (sPTB) were identified in a database of insurance claims, and 568 (12.8%) received 17-OHP. Crude and propensity score-matched recurrence rates and risk ratios (RRs) for the association of receiving 17-OHP on recurrent sPTB were calculated. Results Raw sPTB recurrence rates were higher among those treated versus not treated; after propensity score matching, no association was detected (26.3 vs. 23.8%, RR = 1.1, 95% CI: 0.9–1.4). Conclusion We failed to identify a beneficial effect of 17-OHP for the prevention of spontaneous recurrent preterm birth in our observational, U.S. based cohort. Key Points We observed higher risk for sPTB in the group receiving 17-OHP in the unmatched analysis After propensity-score matching, we still failed to identify a beneficial effect of 17-OHP on sPTB Sensitivity analyses demonstrated robustness to the inclusion criteria and modeling assumptions. [ABSTRACT FROM AUTHOR]

AbstractDue to the difficulties inherent in conducting controlled experiments, recent causal inference studies have focused on developing treatment effect estimation using observational data. One major difficulty in causal inference from observational data is that the underlying causal structure is unknown. This may result in the misidentification of potential sources of causal estimation bias, such as confounders, which must be controlled for accurate estimation. To consider all possible confounders and other relevant information, conventional methods predominantly use all observed covariates indiscriminately. However, previous studies have shown that including all covariates without considering their causal relationships may deteriorate the estimation performance. Although several data-driven variable selection methods have been proposed for treatment effect estimation, they cannot distinguish the confounders from other outcome-related covariates and are limited to simple regression forms. In this study, we propose a method called the Variable Selection Causal Estimation Network (VSCEN) that performs treatment effect estimation and causal variable selection simultaneously. Through end-to-end differentiable training, the VSCEN selects only the covariates beneficial for effect estimation and uses only those selected for effect estimation. Experimental evaluations on fully synthetic, semi-synthetic, and real datasets demonstrate the VSCEN’s superior performance in conditional average treatment effect estimation and competitive performance in average treatment effect estimation along with its accurate variable selection capabilities. [ABSTRACT FROM AUTHOR]

The estimation of heterogeneous treatment effects has attracted considerable interest in many disciplines, most prominently in medicine and economics. Contemporary research has so far primarily focused on continuous and binary responses where heterogeneous treatment effects are traditionally estimated by a linear model, which allows the estimation of constant or heterogeneous effects even under certain model misspecifications. More complex models for survival, count, or ordinal outcomes require stricter assumptions to reliably estimate the treatment effect. Most importantly, the noncollapsibility issue necessitates the joint estimation of treatment and prognostic effects. Model-based forests allow simultaneous estimation of covariate-dependent treatment and prognostic effects, but only for randomized trials. In this paper, we propose modifications to model-based forests to address the confounding issue in observational data. In particular, we evaluate an orthogonalization strategy originally proposed by Robinson (1988, Econometrica) in the context of model-based forests targeting heterogeneous treatment effect estimation in generalized linear models and transformation models. We found that this strategy reduces confounding effects in a simulated study with various outcome distributions. We demonstrate the practical aspects of heterogeneous treatment effect estimation for survival and ordinal outcomes by an assessment of the potentially heterogeneous effect of Riluzole on the progress of Amyotrophic Lateral Sclerosis. [ABSTRACT FROM AUTHOR]

Scalar field ϕ CDM models provide an alternative to the standard Λ CDM paradigm, while being physically better motivated. Dynamical scalar field ϕ CDM models are divided into two classes: the quintessence (minimally and non-minimally interacting with gravity) and phantom models. These models explain the phenomenology of late-time dark energy. In these models, energy density and pressure are time-dependent functions under the assumption that the scalar field is described by the ideal barotropic fluid model. As a consequence of this, the equation of state parameter of the ϕ CDM models is also a time-dependent function. The interaction between dark energy and dark matter, namely their transformation into each other, is considered in the interacting dark energy models. The evolution of the universe from the inflationary epoch to the present dark energy epoch is investigated in quintessential inflation models, in which a single scalar field plays a role of both the inflaton field at the inflationary epoch and of the quintessence scalar field at the present epoch. We start with an overview of the motivation behind these classes of models, the basic mathematical formalism, and the different classes of models. We then present a compilation of recent results of applying different observational probes to constraining ϕ CDM model parameters. Over the last two decades, the precision of observational data has increased immensely, leading to ever tighter constraints. A combination of the recent measurements favors the spatially flat Λ CDM model but a large class of ϕ CDM models is still not ruled out. [ABSTRACT FROM AUTHOR]

Importance The Observational Health Data Sciences and Informatics (OHDSI) is the largest distributed data network in the world encompassing more than 331 data sources with 2.1 billion patient records across 34 countries. It enables large-scale observational research through standardizing the data into a common data model (CDM) (Observational Medical Outcomes Partnership [OMOP] CDM) and requires a comprehensive, efficient, and reliable ontology system to support data harmonization. Materials and methods We created the OHDSI Standardized Vocabularies—a common reference ontology mandatory to all data sites in the network. It comprises imported and de novo -generated ontologies containing concepts and relationships between them, and the praxis of converting the source data to the OMOP CDM based on these. It enables harmonization through assigned domains according to clinical categories, comprehensive coverage of entities within each domain, support for commonly used international coding schemes, and standardization of semantically equivalent concepts. Results The OHDSI Standardized Vocabularies comprise over 10 million concepts from 136 vocabularies. They are used by hundreds of groups and several large data networks. More than 8600 users have performed 50 000 downloads of the system. This open-source resource has proven to address an impediment of large-scale observational research—the dependence on the context of source data representation. With that, it has enabled efficient phenotyping, covariate construction, patient-level prediction, population-level estimation, and standard reporting. Discussion and conclusion OHDSI has made available a comprehensive, open vocabulary system that is unmatched in its ability to support global observational research. We encourage researchers to exploit it and contribute their use cases to this dynamic resource. [ABSTRACT FROM AUTHOR]

Abstract Purpose Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time‐consuming manual case‐finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. Methods We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. Results Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. Conclusions Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real‐time monitoring of novel therapies.

Average causal effects are averages of (heterogeneous) individual treatment effects (ITEs) taken over the entire target population. The estimation of average causal effects has been studied in depth, but averages are insufficient for more individualised decision-making where ITEs are more appropriate. However, estimating ITEs for every population member is challenging, particularly when estimation must be based on observational data rather than data from randomised experiments. One potential problem with observational data arises when there are large differences between the sample distributions of the input features of the treated and control units. This problem is known as covariate shift. It can lead to model misspecification the harmful effects of which can be severe for ITE estimation because point estimation is highly sensitive to regions of the common support of the input space in which the number of treated or control units is very small. Moreover, common solutions are often based on reweighing schemes involving propensity scores which were originally designed for average effects and not ITEs. In this paper, we propose Debiasing Generative Trees, a novel data augmentation method based on generative trees that debiases and undersmooths causal estimators trained on augmented data. It encourages higher modelling complexity that reduces misspecification and improves estimation of ITEs. We show empirically that our proposed approach yields models of higher complexity and more accurate predictions of ITEs, and is competitive with traditional methods for estimating average treatment effects. Our results confirm that reweighing methods can struggle with ITE estimation and that the choice of model class can significantly impact prediction performance.

The objective of causal discovery is to uncover the causal relationships among natural phenomena or human behaviors, thus establishing the basis for subsequent prediction and inference. Traditional ways to reveal the causal structure between variables, such as interventions or random trials, are often deemed costly or impractical. With the diversity of means of data acquisition and abundant data, approaches that directly learn causal structure from observational data are attracting more interest in academia. Unfortunately, learning a directed acyclic graph (DAG) from samples of multiple variables has been proven to be a challenging problem. Recent proposals leverage the continuous optimization method to discover the structure of the target DAG from observational data using an acyclic constraint function. Although it has an elegant mathematical form, its optimization process is prone to numerical explosion or the disappearance of high-order gradients of constraint conditions, making the convergence conditions unstable and the result deviating from the true structure. To tackle these issues, we first combine the existing work to give a series of algebraic equivalent conditions for a DAG and its corresponding brief proofs, then leverage the properties of the M-matrix to derive a new acyclic constraint function. Based on the utilization of this function and linear structural equation models, we propose a novel causal discovery algorithm called LeCaSiM, which employs continuous optimization. Our algorithm exploits the spectral radius of the adjacency matrix to dynamically adjust the coefficients of the matrix polynomial, effectively solving the above problems. We conduct extensive experiments on both synthetic and real-world datasets, the results show our algorithm outperforms the existing algorithms on both computation time and accuracy under the same settings. [ABSTRACT FROM AUTHOR]

Background: Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data. Methods: Two regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status. Results: A total of 13,369 confirmed COVID-19 patients from 70 ICUs were included (with mortality rate of 28%). The optimism-corrected AUROC of the admission tree (with seven paths) was 0.72 (95% CI: 0.71–0.74) and of the 24 h tree (with 11 paths) was 0.74 (0.74–0.77). Both regression trees yielded good calibration and variable selection for both trees was stable. Patient subgroups comprising the tree paths had comparable survival probabilities as the full-fledged logistic regression model, survival probabilities were stable over six COVID-19 surges, and subgroups were shown to have added predictive value over the individual patient variables. Conclusions: We developed and evaluated regression trees, which operate at par with a carefully crafted logistic regression model. The trees consist of homogenous subgroups of patients that are described by simple interpretable constraints on patient characteristics thereby facilitating shared decision-making. [ABSTRACT FROM AUTHOR]

Objective Health data standardized to a common data model (CDM) simplifies and facilitates research. This study examines the factors that make standardizing observational health data to the Observational Medical Outcomes Partnership (OMOP) CDM successful. Materials and methods Twenty-five data partners (DPs) from 11 countries received funding from the European Health Data Evidence Network (EHDEN) to standardize their data. Three surveys, DataQualityDashboard results, and statistics from the conversion process were analyzed qualitatively and quantitatively. Our measures of success were the total number of days to transform source data into the OMOP CDM and participation in network research. Results The health data converted to CDM represented more than 133 million patients. 100%, 88%, and 84% of DPs took Surveys 1, 2, and 3. The median duration of the 6 key extract, transform, and load (ETL) processes ranged from 4 to 115 days. Of the 25 DPs, 21 DPs were considered applicable for analysis of which 52% standardized their data on time, and 48% participated in an international collaborative study. Discussion This study shows that the consistent workflow used by EHDEN proves appropriate to support the successful standardization of observational data across Europe. Over the 25 successful transformations, we confirmed that getting the right people for the ETL is critical and vocabulary mapping requires specific expertise and support of tools. Additionally, we learned that teams that proactively prepared for data governance issues were able to avoid considerable delays improving their ability to finish on time. Conclusion This study provides guidance for future DPs to standardize to the OMOP CDM and participate in distributed networks. We demonstrate that the Observational Health Data Sciences and Informatics community must continue to evaluate and provide guidance and support for what ultimately develops the backbone of how community members generate evidence. [ABSTRACT FROM AUTHOR]

Experience of using meteorological observations and the ERA5 reanalysis for runoff modeling using the GR4J conceptual model is outlined. The study objects are catchments within the Ussuri River basin (Kirovskii, the Russian Far East). The results of the comparison of ground-based observations and reanalysis data are presented. The hydrological model has been calibrated and verified on the basis of various data sources. The traditional scores NSE, logNSE, and BIAS have been used to evaluate the modeling efficiency. According to the scores, the modeling efficiency is generally "satisfactory" and better. It is shown that for simulations, it is better to use observation network data in case of floods and the reanalysis data in case of spring high water and low flow periods. It is concluded that the effective resolution of the ERA5 data for daily precipitation and air temperature for hydrological modeling in the study area is ( 90–120 km). [ABSTRACT FROM AUTHOR]

This study assessed whether permanent supportive housing (PSH) participation is associated with health service use among a population of adults with disabilities, including people transitioning into PSH from community and institutional settings. Our primary data sources were 2014 to 2018 secondary data from a PSH program in North Carolina linked to Medicaid claims. We used propensity score weighting to estimate the average treatment effect on the treated of PSH participation. All models were stratified by whether individuals were in institutional or community settings prior to PSH. In weighted analyses, among individuals who were institutionalized prior to PSH, PSH participation was associated with greater hospitalizations and emergency department (ED) visits and fewer primary care visits during the follow-up period, compared with similar individuals who largely remained institutionalized. Individuals who entered PSH from community settings did not have significantly different health service use from similar comparison group members during the 12-month follow-up period. [ABSTRACT FROM AUTHOR]