Detecting responsible nodes in differential Bayesian networks.

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.

Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances.

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.

Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines.

Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) conducted with non-randomized exposures, published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy.

Unfulfilled and method-specific contraceptive preferences among reproductive-aged contraceptive users in Arizona, Iowa, New Jersey, and Wisconsin.

OBJECTIVE: To identify characteristics associated with unfulfilled contraceptive preferences, document reasons for these unfulfilled preferences, and examine how these unfulfilled preferences vary across specific method users. DATA SOURCES AND STUDY SETTING: We draw on secondary baseline data from 4660 reproductive-aged contraceptive users in the Arizona, Iowa, New Jersey, and Wisconsin Surveys of Women (SoWs), state-representative surveys fielded between October 2018 and August 2020 across the four states. STUDY DESIGN: This is an observational cross-sectional study, which examined associations between individuals' reproductive health-related experiences and contraceptive preferences, adjusting for sociodemographic characteristics. Our primary outcome of interest is having an unfulfilled contraceptive preference, and a key independent variable is experience of high-quality contraceptive care. We also examine specific contraceptive method preferences according to current method used, as well as reasons for not using a preferred method. DATA COLLECTION/EXTRACTION METHODS: Survey respondents who indicated use of any contraceptive method within the last 3 months prior to the survey were eligible for inclusion in this analysis. PRINCIPAL FINDINGS: Overall, 23% reported preferring to use a method other than their current method, ranging from 17% in Iowa to 26% in New Jersey. Young age (18-24), using methods not requiring provider involvement, and not receiving quality contraceptive care were key attributes associated with unfulfilled contraceptive preferences. Those using emergency contraception and fertility awareness-based methods had some of the highest levels of unfulfilled contraceptive preferences, while pills, condoms, partner vasectomy, and IUDs were identified as the most preferred methods. Reasons for not using preferred contraceptive methods fell largely into one of two buckets: system-level or interpersonal/individual reasons. CONCLUSIONS: Our findings highlight that avenues for decreasing the gap between contraceptive methods used and those preferred to be used may lie with healthcare providers and funding streams that support the delivery of contraceptive care.

Markov modeling for cost-effectiveness using federated health data network.

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.

Association of Premorbid GLP-1RA and SGLT-2i Prescription Alone and in Combination with COVID-19 Severity.

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.

Use and reporting of inverse-probability-of-treatment weighting for multicategory treatments in medical research: a systematic review.

OBJECTIVES: Causal inference methods for observational data represent an alternative to randomised controlled trials when they are not feasible or when real-world evidence is sought. Inverse-probability-of-treatment weighting (IPTW) is one of the most popular approaches to account for confounding in observational studies. In medical research, IPTW is mainly applied to estimate the causal effect of a binary treatment, even when the treatment has in fact multiple categories, despite the availability of IPTW estimators for multiple treatment categories. This raises questions about the appropriateness of the use of IPTW in this context. Therefore, we conducted a systematic review of medical publications reporting the use of IPTW in the presence of a multi-category treatment. Our objectives were to investigate the frequency of use and the implementation of these methods in practice, and to assess the quality of their reporting. STUDY DESIGN AND SETTING: Using Pubmed, Embase and Web of Science, we screened 5660 articles and retained 106 articles in the final analysis that were from 17 different medical areas. This systematic review is registered on PROSPERO (CRD42022352669). RESULTS: The number of treatment groups varied between 3 and 9, with a large majority of articles (90 [84.9%]) including 3 or 4 groups. The most commonly used method for estimating the weights was multinomial regression (51 [48.1%]) and generalized boosted models (48 [45.3%]). The covariates of the weight model were reported in 91 articles (85.9 %). Twenty-six articles (24.5 %) did not discuss the balance of covariates after weighting, and only 16 articles (15.1 %) referred to the assumptions needed to obtain correct inferences. CONCLUSION: The results of this systematic review illustrate that medical publications scarcely use IPTW methods for more than two treatment categories. Among the publications that did, the quality of reporting was suboptimal, in particular in regard to the assumptions and model building. IPTW for multi-category treatments could be applied more broadly in medical research, and the application of the proposed guidelines in this context will help researchers to report their results and to ensure reproducibility of their research.

Association of hospital-based substance use supports on emergency department revisits: a retrospective cohort study in Sudbury, Canada from 2018 to 2022.

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.

Evaluating the Practicality of Causal Inference From Non-randomized Observational Data in Small-Scale Clinical Settings: A Study on the Effects of Ninjin'yoeito.

Objective The primary goal of this study was to demonstrate the practical application of causal inference using non-randomized observational data, adapting this approach to smaller populations, such as those in hospitals or community healthcare. This adaptation seeks a more effective and practical research method than randomized controlled trials (RCTs), with the goal of revealing novel insights unexplored by traditional research and enhancing understanding within the realm of causal inference. Methods This study evaluated the effects of Ninjin'yoeito (NYT), a traditional Japanese Kampo medicine, on Overactive Bladder Symptom Score (OABSS) and the frailty scores. Employing new statistical methods, this study sought to illustrate the efficacy of estimating causal relationships from non-randomized data in a clinical setting. The database included 985 women aged 65-90 years who visited a clinic between November 2016 and November 2022. By utilizing various statistical techniques, including regression analysis, inverse probability of treatment weighting (IPTW), instrumental variable (IV), and difference-in-differences (DiD) analysis, this study aimed to provide insights beyond traditional methods, attempting to bridge the gap between theory and practice in causal inference. Results After applying propensity score matching, the NYT treatment group (220 participants) and non-treatment group (182 participants) were each adjusted to two groups of 159 individuals. NYT significantly improved OABSS and frailty scores. IPTW analysis highlighted that on average, the NYT treatment group showed an improvement of 0.8671 points in OABSS and 0.1339 points in the frailty scores, surpassing the non-treatment group (p<0.05). IV analysis indicated that NYT treatment is predicted to increase ΔOABSS by an average of approximately 4.86 points, highlighting its significant positive impact on OABSS improvement. The DiD analysis showed that the NYT treatment group demonstrated an average improvement of 0.5457 points in OABSS, which was significantly higher than that of the control group. The adjusted R² value for the model is 0.025. Conclusion This study successfully implemented a practical application of causal inference using non-randomized observational data in a relatively small population. NYT showed a significant improvement in OABSS and vulnerability, and this result was confirmed using a new statistical method. The relatively low adjusted R² of the model suggests the existence of other unmeasured variables that influence OABSS and vulnerability improvement. In particular, the use of diverse statistical techniques, including IPTW, IV, and DiD analysis, is an important step toward revealing the effectiveness of inferring causal relationships from non-randomized data and narrowing the gap between theory and practice. This study provides a valid and practical alternative to RCTs and reveals new insights that have not been explored in traditional research.

Using joint models for longitudinal and time-to-event data to investigate the causal effect of salvage therapy after prostatectomy.

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.

Heterogeneous treatment effect estimation for observational data using model-based forests.

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.

A Tutorial on Net Benefit Regression for Real-World Cost-Effectiveness Analysis Using Censored Data from Randomized or Observational Studies.

HIGHLIGHTS: We 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.

OHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization.

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.

Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges.

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.

Is glucocorticoid bridging therapy associated with later use of glucocorticoids and biological DMARDs during the disease course of patients with rheumatoid arthritis in daily practice? A real-world data analysis.

OBJECTIVE: To evaluate if initially starting glucocorticoid (GC) bridging leads to a higher probability of long-term GC and biological (b)DMARD use in rheumatoid arthritis (RA)-patients. METHODS: Electronical health records data from newly diagnosed RA-patients from the Leiden University Medical Center were used. Patients who started GC as part of initial treatment (iGC group) and who did not (niGC group) were compared in terms of GC and bDMARD use later in the disease course. Multivariable adjustment was performed to account for confounding by indication. RESULTS: 465/932 newly diagnosed RA-patients (50 %) were treated with GC as initial treatment step. Patients in the iGC group were older, included fewer females, had a higher disease activity at baseline compared to the niGC group plus a more rapid decrease in DAS28 in the first 6 months. During follow-up, 42 % of the iGC group started a second course of GC and 17 % started a bDMARD, compared to 34 % and 13 % In the niGC group. The hazard to start a bDMARD later in the disease course was not significantly different between the two groups in two time periods (0.34 95 %CI(0.09;1.21) resp. 1.48 95 %CI (0.98;2.22)), but the hazard to (re)start GC later on was higher for the iGC group (aHR 1.37 95 %CI(1.09;1.73)). CONCLUSION: In this daily practice cohort of newly diagnosed RA patients, patients in the iGC group had a more rapid DAS28 decrease and an increased probability of starting GC later on compared to the niGC group. The probability of bDMARD use was not significantly increased.

cometrics: A New Software Tool for Behavior-analytic Clinicians and Machine Learning Researchers.

Cometrics is a Microsoft Windows compatible clinical tool for the collection and recording of frequency- and duration-based target behaviors, physiological signals, and video data. This software package is designed to record in-vivo observational and physiological data. In addition, we have included features that allow observers to capture video from real-time camera feeds and import saved video for retroactive data collection. By using Microsoft Excel-based spreadsheets, also called keystroke files, assessment and treatment sessions are exported into a single document using the click of a button. Integrated interobserver agreement metrics allow comparisons across primary and reliability observers, with the output exported into a spreadsheet for easy reference. All file system interactions are handled by the user interface, so files and folders are created and managed without manual intervention. This software is available free-of-charge through the Microsoft Store for Windows 10 and 11 and the source code is publicly available on GitHub. Supplementary Information: The online version contains supplementary material available at 10.1007/s40617-023-00817-w.