Causal Analysis Methods Research Articles

Genetically predicted 486 blood metabolites concerning risk of systemic lupus erythematosus: a Mendelian randomization study

Metabolic abnormalities constitute a significant characteristic of systemic lupus erythematosus (SLE). We utilised a two-sample Mendelian randomisation (MR) study to evaluate the potential causal association between 486 blood metabolites and SLE. Exposure data at the metabolite level were extracted from 7824 European Genome-wide association studies (GWAS). Preliminary analysis utilised SLE GWAS data from FinnGen. The primary method for causal analysis relied on random inverse variance weighting (IVW). To ensure robustness, sensitivity analyses included the Cochran Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis. Steiger testing and linkage disequilibrium score regression were employed to validate the identified metabolites. This study identified 12 metabolites, comprising six known chemical structures: 1,5-anhydroglucitol(1,5-AG) [odds ratio (OR) = 0.100, 95% confidence interval (CI): 0.015–0.773, P = 0.027), gamma-glutamylthreonine (OR = 0.077, 95% CI: 0.010–0.574, P = 0.012), 5-dodecenoate(12:1n7) (OR = 0.205, 95% CI: 0.061–0.685, P = 0.010), linoleoylglycerophosphoethanolamine * (OR = 0.159, 95% CI: 0.027–0.933, P = 0.044), erythrose (OR = 88.331,95% CI:1.098–63.214, P = 0.040) and 1-, adrenate (22:4n6) (OR = 9.876, 95% CI: 1.753–55.639, P = 0.001)]. Additionally, we found associations between SLE and six unknown chemical structures: X-06351 (OR = 0.071, 95% CI: 0.006–0.817, P = 0.034), X-10810 (OR = 4.268 95% CI: 1.260–14.459, P = 0.020), X-11412 (OR = 5.418 95% CI: 1.068–27.487, P = 0.041), X-11905 (OR = 0.551, 95%CI: 0.304–0.997, P = 0.049), X-12038 (OR = 0.178 95%CI: 0.032–0.988, P = 0.045), X-12217 (OR = 0.174 95%CI: 0.044–0.680, P = 0.014). This study offers evidence supporting a causal relationship between SLE and 12 circulating metabolites, six of which have known chemical structures and six that remain unidentified. These findings introduce a new perspective for further exploration of SLE mechanisms.

Improving the institutional environment for pilot testing of innovative technologies in Russia and abroad

Aim. To propose scientifically substantiated approaches to improving and accelerating the development of the institutional environment of pilot testing of new technologies in Russia.Objectives. To analyze the current state and highlight the main problems of the experimental-industrial infrastructure of innovation activity in Russia and abroad; to present tools for improvement taking into account international experience and geopolitical challenges.Methods. The study uses the methods of analysis and synthesis of state programs to support the development of experimental-industrial infrastructure, the method of causal analysis of barriers to the formation of experimental-industrial infrastructure, as well as methods of tabular and graphical structuring and interpretation of data.Results. By analyzing the limiting factors of the development of pilot industrial infrastructure on the example of China, India and Western countries the general barriers inherent in these territories and specific barriers formed by geographical, political or cultural peculiarities are revealed. The analysis of tools for the development of pilot industrial infrastructure in Russia is presented, taking into account which the necessary measures of state support and its main beneficiaries are proposed and justified.Conclusions. In Russia in the last few years there has been a decrease in the number of research, design, engineering, design and survey organizations, pilot plants. The existing practice of functioning and development of experimental activities, weak dynamics of its development in the conditions of realization of the course on technological sovereignty in the country speak about the insufficient volume of state instruments of stimulation of experimental-industrial tests and development of experimental-industrial infrastructure. There is a need for a transition from the implementation of private initiatives to a systematic approach to the development of this activity with the involvement of the state, science, small and medium-sized businesses. In order to scale up the developed domestic practices and obtain tangible results for the economy, it is advisable to increase the financing of pilot industrial activities in comparable amounts with international practices.

Causal relationship analysis of high-dimensional time series based on quantile factor model

Finding the causal relationship between variables from observed data is a key issue in many decision analytics researches. Because the traditional Granger causality model is affected by the curse of dimensionality, it is difficult to accurately find causality in high-dimensional time series. To address this problem, we propose a new Granger causality analysis method based on quantile factor model, named QFM-CGC. First, QFM-CGC adopts Akaike information criterion to select models, which avoids setting the lag order relying on human intervention. Then, the quantile factor model is established to reduce the dimensionality of conditional variables in a VAR (Vector Auto-Regressive) model, further reducing the number of coefficients. After that, the reduced-dimensional VAR model is used for a conditional Granger causality analysis. Finally, in order to identify the connectivity structure between the underlying system and observation time series, Monte Carlo simulation is applied to evaluate the performance of different methods. Experiments compared with classical methods on linear simulation systems in different dimensions and two realistic datasets, all demonstrate the effectiveness of QFM-CGC. In simulation experiments, the method proposed in this article has improved the accuracy of identifying causal relationships by an average of 6% and 3.46%. In two real-world data experiments, compared with the optimal comparison methods, QFM-CGC reduced the prediction error of causal relationship sequences.

Hierarchical Bayesian Causality Network to Extract High-Level Semantic Information in Visual Cortex.

Functional MRI (fMRI) is a brain signal with high spatial resolution, and visual cognitive processes and semantic information in the brain can be represented and obtained through fMRI. In this paper, we design single-graphic and matched/unmatched double-graphic visual stimulus experiments and collect 12 subjects' fMRI data to explore the brain's visual perception processes. In the double-graphic stimulus experiment, we focus on the high-level semantic information as "matching", and remove tail-to-tail conjunction by designing a model to screen the matching-related voxels. Then, we perform Bayesian causal learning between fMRI voxels based on the transfer entropy, establish a hierarchical Bayesian causal network (HBcausalNet) of the visual cortex, and use the model for visual stimulus image reconstruction. HBcausalNet achieves an average accuracy of 70.57% and 53.70% in single- and double-graphic stimulus image reconstruction tasks, respectively, higher than HcorrNet and HcasaulNet. The results show that the matching-related voxel screening and causality analysis method in this paper can extract the "matching" information in fMRI, obtain a direct causal relationship between matching information and fMRI, and explore the causal inference process in the brain. It suggests that our model can effectively extract high-level semantic information in brain signals and model effective connections and visual perception processes in the visual cortex of the brain.

Solutions to Food Security Problems in Indonesia: An Islamic Perspective

Indonesia should not have problems related to food security. However, the level of food production is not so large that it depends on rice imports. Therefore, this study aims to describe the various factors causing the food security crisis in Indonesia and the solutions according to an Islamic perspective. This study used the descriptive analysis method. There are three steps in this method, the first is the provision of data from BPS and various relevant literature. Second, the data were analyzed using the causality analysis method. Third, the presentation of the results of the analysis is presented in an explanatory descriptive manner with an Islamic perspective. The results of the study show that the main cause of the food security crisis in Indonesia is the neoliberalism paradigm and its governance concept. The derivative factors that cause the food crisis are the lack of swift management: sources of funds, infrastructure, quality of human resources, technology, and mechanisms or regulations that facilitate access for all people to all economic factors. The solution from an Islamic perspective is the paradigm of food independence which creates food security. Several recommendations for food independence are the availability of agricultural land by the Islamic ownership system; industrial political policies that support food security; and independent research to formulate maps, directions, financing, and implementation.

Novel Causal Inference Method Estimates Treatment Effects of Contemporary Drugs in a Global Cohort of Patients with Relapsed and Refractory Mature T-Cell and NK-Cell Neoplasms

There is no universal approach to treat patients with relapsed and refractory (RR) mature T-cell and NK-cell neoplasms (TNKL). While participation in a clinical trial is favored, appropriate options are often unavailable. Thus, outside of a trial, physicians may rely on real world evidence when making a treatment choice. We hypothesized that innovative causal predictive models that utilize existing clinical information could estimate comparative efficacy of various treatments and support clinical decision making. As the first step, we built a large global cohort of 925 patients with RR TNKL diagnosed between Jan 2010 and Dec 2020 representing 9 countries. Eligible patients received either a single agent (SA) such as an epigenetic modifier, small molecule inhibitor, brentuximab vedotin (BV) among others, or platinum-, gemcitabine-, ifosfamide-based cytotoxic chemotherapy (CC) in the second line setting. Patients were followed from lymphoma diagnosis, or first retreatment start date to death or lost to follow-up. Thirteen covariates [relapsed/refractory state, with refractory defined as lack of complete remission (CR) to first line therapy, country Japan, age >50 years, histological subtype AITL, IPI, PIT score, response to 1 st line treatment, 2 nd line treatment (SA or CC), response to 2 nd line treatment, autologous stem cell transplantation (Auto-SCT), response to ASCT, allogeneic stem cell transplantation (Allo-SCT), response to Allo-SCT] were identified to have an independent prognostic effect on overall survival (OS). Based on measures of Brier score (BS) and Concordance index (CI), eight of them were selected for multivariate Cox regression model. We observed comparable performance of the Cox models utilizing these 8 covariates (CI: 0.755; BS:0.174) versus 5 of these 8 covariates (CI:0.744; BS:0.186). Hence 5 covariates were utilized for further OS analyses. We found that multivariate Cox outperforms other traditional models including random survival forest and gradient boosted (Table 1). Since data under consideration is observational and not derived from a clinical trial, there may be several (unobserved) confounders leading to bias in the outcomes, which are not adjusted for by these traditional approaches. We introduce a novel causal method, Synthetic Intervention (SI), which adjusts for such biases. For comparative evaluation of SI with multivariate Cox, due to data requirement for SI, we chose only two covariates [response to 1 st line stratified as CR, partial remission (PR), stable disease (SD) and progressive disease (PD) and administration of second line treatment (SA or CC)] (Table 1). We found that SI outperforms multivariate Cox. Further, despite heterogeneity in OS across the 8 cohorts, the median OS prediction of SI was comparable to actual median OS for each cohort, thus highlighting robustness of SI (Figure 1). All the performance evaluations were done using 60:40 train: test split, repeated 500 times. We confirmed previous reports that relapsed patients had superior OS compared to primary refractory patients across common histologies: PTCL-NOS (p<.0001), AITL (p=.036), ENKTCL (p=.027), and ALK- ALCL (p=.001). Those who received SA at first retreatment had superior OS compared to those treated with CC in AITL (p<.0005) and ALK- ALCL (p=.036) and comparable OS in PTCL-NOS, ENKTCL, and ALK+ ALCL. In ALK- ALCL, those treated with BV had superior OS compared to CC (p=.027). Patients with AITL treated with small molecule inhibitors (SMI) had greater OS compared to those treated with CC (p<.0005) and epigenetic modifiers (EM) (p=.001), with the most common SMIs including alisertib, duvelusib, cerdulatinib, and cyclosporine. Those who responded to EM and BV (CR+PR+SD) had superior OS compared to non-responders (PD) in PTCL-NOS (p=.004) and ALK- ALCL (p<0.0005). Among patients who underwent SCT after second line therapy, those bridged with SA had comparable OS to CC in PTCL-NOS and an improved trend (p=.073) in AITL. In summary, we have characterized the largest global cohort of RR patients with TNKL in the era of contemporary therapies. As a causal analysis method, SI demonstrates the causal effect superiority of SA over CC in terms of survival outcomes for patients who responded differently to their 1st line treatment. Moreover, it achieves prediction accuracy comparable to, if not better than, Cox regressions, one of the most widely used methods in survival analysis.

AUTHENTIC CHEESES: MICROBIOLOGY, STARTERS, AND TECHNOLOGICAL ASPECTS OF PRODUCTION

The work is dedicated to the varieties, microbiological peculiarities and technological aspects of production of certain types of authentic cheeses. The purpose of work is to determine the microbiological peculiarities of authentic cheeses, to analyze their varieties and microbiota, to define the biotechnological peculiarities of production of certain types of authentic cheeses. Monographic, constructive, causal analysis and logical generalization methods were used. Cheese is the most popular food product in the world; there are 5,000 varieties of cheese. Although standard cheeses produced on large-scale productions are the most popular and widespread, authentic cheeses are also popular due to their unique organoleptic properties. Many factors influence the unique characteristics of authentic cheeses, the main of which are the quality of milk, its microbiological and chemical composition, and the technological methods used in cheese-making. All authentic cheeses are characterized by the presence of lactic acid bacteria, but the use of untreated milk sometimes leads to the presence of foodborne pathogens in the final product. Certain authentic cheeses have a complex microbiota, which complicates their production and the creation of starter cultures. However, for many cheeses, especially those like feta, there are now technological approaches to production, and most research is aimed at achieving an authentic taste and improving the organoleptic properties of the final product. Analysis of the quantitative and qualitative composition of the fermenting microbiota and the technological features of different types of traditional cheeses makes it possible to determine the composition of new fermenting compositions that are inherent to a particular product and to choose ways to increase the efficiency of cheese production.

The association of metabolic syndrome with rotator cuff tendinopathy: a two-sample Mendelian randomization study

BackgroundObservational research reported the underlying correlation of metabolic syndrome (MetS) and its components with rotator cuff tendinopathy (RCT), but their causality remained unclear. This study aimed to investigate whether genetically predicted MetS was related to the risk of RCT.MethodsBoth univariable and multivariable Mendelian randomization (MR) analysis was applied using summary-level data from the most comprehensive genome-wide association studies to estimate the associations of MetS and its component with RCT, with the inverse variance weighted (IVW) as the primary method, and the method of Causal Analysis Using Summary Effect Estimates (CAUSE) as a supplement for false positives detection. The mediation analysis was furtherly used for the assessment of direct and indirect effects.ResultsUnivariable analysis revealed that genetically predicted MetS (OR: 1.0793; 95% CI 1.0311 to 1.1297), body mass index (BMI) (OR 1.2239; 95% CI 1.1357 to 1.3189), and waist circumference (WAC) (OR 1.3177; 95% CI 1.2015 to 1.4451) had a significant positive association with the risk of RCT. Triglycerides and systolic blood pressure were suggestively associated with RCT risk. These associations were also identified by CAUSE. There was independent causality of BMI (OR: 1.1806; 95% CI 1.0788 to 1.2920) and WAC (OR 1.3716; 95% CI 1.2076 to 1.5580) on RCT after adjustment for confounders. No mediator was found in the causal associations.ConclusionOur study revealed the genetic causality of MetS and its components, especially BMI and WAC, with RCT risk. Early prevention and diagnosis of excess central adiposity contributing to MetS are significant in the RCT risk management.

The effect of smoking on latent hazard classes of metabolic syndrome using latent class causal analysis method in the Iranian population

BackgroundThe prevalence of metabolic syndrome is increasing worldwide. Clinical guidelines consider metabolic syndrome as an all or none medical condition. One proposed method for classifying metabolic syndrome is latent class analysis (LCA). One approach to causal inference in LCA is using propensity score (PS) methods. The aim of this study was to investigate the causal effect of smoking on latent hazard classes of metabolic syndrome using the method of latent class causal analysis.MethodsIn this study, we used data from the Tehran Lipid and Glucose Cohort Study (TLGS). 4857 participants aged over 20 years with complete information on exposure (smoking) and confounders in the third phase (2005–2008) were included. Metabolic syndrome was evaluated as outcome and latent variable in LCA in the data of the fifth phase (2014–2015). The step-by-step procedure for conducting causal inference in LCA included: (1) PS estimation and evaluation of overlap, (2) calculation of inverse probability-of-treatment weighting (IPTW), (3) PS matching, (4) evaluating balance of confounding variables between exposure groups, and (5) conducting LCA using the weighted or matched data set.ResultsBased on the results of IPTW which compared the low, medium and high risk classes of metabolic syndrome (compared to a class without metabolic syndrome), no association was found between smoking and the metabolic syndrome latent classes. PS matching which compared low and moderate risk classes compared to class without metabolic syndrome, showed that smoking increases the probability of being in the low-risk class of metabolic syndrome (OR: 2.19; 95% CI: 1.32, 3.63). In the unadjusted analysis, smoking increased the chances of being in the low-risk (OR: 1.45; 95% CI: 1.01, 2.08) and moderate-risk (OR: 1.68; 95% CI: 1.18, 2.40) classes of metabolic syndrome compared to the class without metabolic syndrome.ConclusionsBased on the results, the causal effect of smoking on latent hazard classes of metabolic syndrome can be different based on the type of PS method. In adjusted analysis, no relationship was observed between smoking and moderate-risk and high-risk classes of metabolic syndrome.

Causal relationship between spondylarthritis and stroke in a European population: a two sample Mendelian randomization study.

Observational studies have found an increased risk of stroke in patients with spondyloarthritis, but the results are susceptible to reverse causality and confounders. Therefore, the study aimed to further explore the association between spondyloarthritis and different subtypes of stroke by using a two sample Mendelian randomization (MR) analysis. Genetic instrumental variables for spondyloarthritis were identified using summary level data from a genome-wide association study involving 201,581 people. Summary statistics from the Multiancestry Genome-wide Association Study of Stroke Consortium were used to obtain genetic data on stroke. There was no sample overlap between the exposure and outcome datasets. Inverse-variance weighted was considered the primary MR method for causal analysis. Heterogeneity, pleiotropy and sensitivity analyses were performed to ensure robustness, and single nucleotide polymorphism (SNP) with potential confounders was further screened in the PhenoScanner database to better evaluate the stability of our study. One SNP (rs1065045) was excluded due to schizophrenia. After excluding SNP (rs1065045), results of the second MR analysis were slightly different from the first, which were considered as the final result: a significant positive causality between spondyloarthritis and cardioembolic stroke (OR=1.296, 95% CI:1.094-1.534, p=0.003); a possible positive causality between spondyloarthritis and any stroke (OR=1.082, 95% CI:1.016-1.152, p=0.013)/any ischemic stroke (OR=1.086, 95% CI:1.013-1.163, p=0.020); no significant/possible causality between spondyloarthritis and small vessel stroke (OR=1.168, 95% CI:0.993-1.375, p=0.061). Insufficient power may be one possible reason why a causality was not observed between spondyloarthritis in our study. This study suggests that the possible causative effects of spondyloarthritis predicted by genetics on stroke may be limited to any stroke, any ischemic stroke, and cardioembolic stroke, especially the last.

Causal effects of walking pace on osteoarthritis: a two-sample mendelian randomization study.

Background: Osteoarthritis (OA) is one of the most common joint diseases worldwide, imposing a substantial burden on individuals and society. Numerous pieces of evidence suggest that walking pace (WP) can serve as a predictive indicator for the risk of various diseases, and observational studies have also found a potential link between WP and the risk of OA. However, the causal relationship between WP and the risk of OA remains unclear. Methods: We conducted a mendelian randomization (MR) study using data from the European Genome-wide Association Study, which included WP (including 459,915 participants), OA (including 10,083 cases and 40,425 controls), knee OA (including 24,955 cases and 378,169 controls), and hip OA (including 15,704 cases and 378,169 controls). Single nucleotide polymorphisms (SNPs) associated with WP were utilized to infer causal associations with OA and its subtypes. The Inverse Variance Weighted (IVW) technique served as the primary causal analysis method. Three auxiliary MR methods - MR-Egger, weighted median, and maximum likelihood - were used to substantiate the IVW results. Sensitivity analyses were performed to examine heterogeneity and pleiotropy. In addition, multivariate MR (MVMR) analysis was used to assess causality after adjustment for three potential confounders. Results: According to the results of the IVW method, every 1 standard deviation increased in genetic WP corresponds to an 89% reduction in the risk of OA (odds ratio (OR) = 0.11; 95% confidence interval (CI), 0. 06-0.19; p = 1.57 × 10-13), an 83% reduction in the risk of knee OA (OR = 0.17; 95% CI, 0.11-0.28; p = 2.78 × 10-13), and a 76% reduction in the risk of hip OA (OR = 0.24; 95% CI, 0.14-0.43; p = 1.51 × 10-6). These results were confirmed by the three additional MR methods and validated by the sensitivity analysis. Ultimately, the MVMR analysis confirmed that the role of WP in reducing the risk of OA and its subtypes remains consistent regardless of potential confounders. Conclusion: The results of our MR study highlight a significant causal association between WP and the susceptibility to OA, including its knee and hip subtypes. These findings propose that WP could be utilized as a potential prognostic factor for OA risk.

Letters of a Siberian soldier - a source on the history of the Great Patriotic War of 1941-1945

The subject of the study is 1941-1944 letters of participant of the Great Patriotic War, Novosibirsk historian Fyodor Sergeyevich Merkuryev. The object of research is frontline letters as a historical source. The purpose of the work is to determine the scientific and cognitive value of F. S. Merkuryev's letters as a source on the history of the Great Patriotic War. In the course of the research, the comparative historical method, the method of causal analysis between events, as well as the method of source studies were used. The informational component of F. S. Merkuryev's letters was determined using the method of textual analysis, and the problem-chronological method allowed to identify the features of letters depending on the time of their writing. As a result of the analysis of almost 300 front-line letters that were not previously introduced into scientific circulation, valuable information was obtained that reveals various aspects of life at the front and in the rear, as well as allowing to trace the combat path of the 49th Cavalry Division formed in Siberia, as well as to get an idea of the specifics of military field living conditions and other details of front-line everyday life. Conclusions are drawn about the significant informational value of the presented epistolary source for the study of certain aspects of the history of the Great Patriotic War, as well as the need for further work on the introduction of such ego documents into scientific circulation.

Description length guided nonlinear unified Granger causality analysis.

Most Granger causality analysis (GCA) methods still remain a two-stage scheme guided by different mathematical theories; both can actually be viewed as the same generalized model selection issues. Adhering to Occam's razor, we present a unified GCA (uGCA) based on the minimum description length principle. In this research, considering the common existence of nonlinearity in functional brain networks, we incorporated the nonlinear modeling procedure into the proposed uGCA method, in which an approximate representation of Taylor's expansion was adopted. Through synthetic data experiments, we revealed that nonlinear uGCA was obviously superior to its linear representation and the conventional GCA. Meanwhile, the nonlinear characteristics of high-order terms and cross-terms would be successively drowned out as noise levels increased. Then, in real fMRI data involving mental arithmetic tasks, we further illustrated that these nonlinear characteristics in fMRI data may indeed be drowned out at a high noise level, and hence a linear causal analysis procedure may be sufficient. Next, involving autism spectrum disorder patients data, compared with conventional GCA, the network property of causal connections obtained by uGCA methods appeared to be more consistent with clinical symptoms.

Hypertension and Stroke as Mediators of Air Pollution Exposure and Incident Dementia

Fine particulate matter air pollution (PM2.5) has been consistently associated with cardiovascular disease, which, in turn, is associated with an increased risk of dementia. As such, vascular dysfunction might be a mechanism by which PM2.5 mediates dementia risk, yet few prior epidemiological studies have examined this potential mechanism. To investigate whether hypertension and stroke serve as mediators and modifiers of the association of PM2.5 with incident dementia. As part of the Environmental Predictors of Cognitive Health and Aging (EPOCH) Project, this cohort study used biennial survey data collected between 1998 and 2016 from respondents of the Health and Retirement Study (HRS), a nationally representative, population-based, cohort in the US. Eligible participants were those over 50 years of age who were free of dementia at baseline and had complete exposure, mediator, outcome, and demographic data from the HRS. Data analysis was conducted from August to November 2022. Exposure to PM2.5, calculated for the 10 years preceding each person's baseline examination according to residential histories and spatiotemporal models. Incident dementia was identified using a validated algorithm based on cognitive testing and informant reports. The 4-way decomposition causal mediation analysis method was used to quantify the degree to which hypertension and stroke mediated or modified the association of PM2.5 with incident dementia after adjustment for individual-level and area-level covariates. Among 27 857 participants (mean [SD] age at baseline, 61 [10] years; 15 747 female participants [56.5%]; 19 249 non-Hispanic White participants [69.1%]), 4105 (14.7%) developed dementia during the follow-up period (mean [SD], 10.2 [5.6] years). Among participants with dementia, 2204 (53.7%) had a history of hypertension at baseline and 386 (9.4%) received a diagnosis of hypertension during the follow up. A total of 378 participants (9.2%) had a history of stroke at baseline and 673 (16.4%) developed stroke over the follow-up period. The IQR of baseline PM2.5 concentrations was 10.9 to 14.9 μg/m3. In fully adjusted models, higher levels of PM2.5 (per IQR) were not associated with increased risk of incident dementia (HR, 1.04; 95% CI, 0.98 to 1.11). Although there were positive associations of prevalent stroke (HR, 1.67; 95% CI, 1.48 to 1.88) and hypertension (HR, 1.15; 95% CI, 1.08 to 1.23) with incident dementia compared with those free of stroke and hypertension during follow-up, there was no statistically significant association of PM2.5 with stroke (odds ratio per IQR increment in PM2.5, 1.08; 95%CI, 0.91 to 1.29) and no evidence of an association of PM2.5 with hypertension (odds ratio per IQR increment in PM2.5, 0.99; 95%CI, 0.92 to 1.07). Concordantly, there was no evidence that hypertension or stroke acted as mediators or modifiers of the association of PM2.5 with incident dementia. Although the nonmediated interaction between PM2.5 and hypertension accounted for 39.2% of the total excess association (95% CI, -138.5% to 216.9%), the findings were not statistically significant. These findings suggest that although hypertension may enhance the susceptibility of individuals to air pollution, hypertension and stroke do not significantly mediate or modify the association of PM2.5 with dementia, indicating the need to investigate other pathways and potential mediators of risk.

Propensity score weighting methods for causal subgroup analysis with time-to-event outcomes.

Evaluating causal effects of an intervention in pre-specified subgroups is a standard goal in comparative effectiveness research. Despite recent advancements in causal subgroup analysis, research on time-to-event outcomes has been lacking. This article investigates the propensity score weighting method for causal subgroup survival analysis. We introduce two causal estimands, the subgroup marginal hazard ratio and subgroup restricted average causal effect, and provide corresponding propensity score weighting estimators. We analytically established that the bias of subgroup-restricted average causal effect is determined by subgroup covariate balance. Using extensive simulations, we compare the performance of various combinations of propensity score models (logistic regression, random forests, least absolute shrinkage and selection operator, and generalized boosted models) and weighting schemes (inverse probability weighting, and overlap weighting) for estimating the causal estimands. We find that the logistic model with subgroup-covariate interactions selected by least absolute shrinkage and selection operator consistently outperforms other propensity score models. Also, overlap weighting generally outperforms inverse probability weighting in terms of balance, bias and variance, and the advantage is particularly pronounced in small subgroups and/or in the presence of poor overlap. We applied the methods to the observational Comparing Options for Management: PAtient-centered REsults for Uterine Fibroids study to evaluate the causal effects of myomectomy versus hysterectomy on the time to disease recurrence in a number of pre-specified subgroups of patients with uterine fibroids.

Causal discovery analysis: A promising tool in advancing precision medicine for eating disorders.

Precision medicine (i.e., individually tailored treatments) represents an optimal goal for treating complex psychiatric disorders, including eating disorders. Within the eating disorders field, most treatment development efforts have been limited in their ability to identify individual-level models of eating disorder psychopathology and to develop and apply an individually tailored treatment for a given individual's personalized model of psychopathology. In addition, research is still needed to identify causal relationships within a given individual's model of eating disorder psychopathology. Addressing this limitation of the current state of precision medicine-related research in the field will allow us to progress toward advancing research and practice for eating disorders treatment. We present a novel set of analytic tools, causal discovery analysis (CDA) methods, which can facilitate increasingly fine-grained, person-specific models of causal relations among cognitive, behavioral, and affective symptoms. CDA can advance the identification of an individual's causal model that maintains that individuals' eating disorder psychopathology. In the current article, we (1) introduce CDA methods as a set of promising analytic tools for developing precision medicine methods for eating disorders including the potential strengths and weaknesses of CDA, (2) provide recommendations for future studies utilizing this approach, and (3) outline the potential clinical implications of using CDA to generate personalized models of eating disorder psychopathology. CDA provides a novel statistical approach for identifying causal relationships among variables of interest for a given individual. Person-specific causal models may offer a promising approach to individualized treatment planning and inform future personalized treatment development efforts for eating disorders.

Spatio-temporal changes in the causal interactions among Sustainable Development Goals in China

Extensive efforts have been dedicated to deciphering the interactions associated with Sustainable Development Goals (SDGs). However, these developments are hampered by a lack of efficient strategies to avoid beneficial synergies being offset by harmful trade-offs. To fill these gaps, we used causal diagnosis and network analysis methods to construct 1302 directed networks of SDGs for 31 provinces in China from 2000 to 2020. We observed a dramatic offsetting effect of SDG synergies and trade-offs in China from 2000 to 2020, with approximately 27% of trade-off indicator pairs turning into synergies and about 25% of the synergy indicator pairs turning into trade-offs. However, our findings suggested that prioritising the progress of high-frequency indicators in virtuous cycles could multiply the positive systemic effects of the SDGs. Moreover, controlling the transition from passive to active in the trade-off network of SDGs remains a challenge in advancing the SDGs holistically.

Choosing an Optimal Method for Causal Decomposition Analysis with Continuous Outcomes: A Review and Simulation Study

Causal decomposition analysis is among the rapidly growing number of tools for identifying factors (“mediators”) that contribute to disparities in outcomes between social groups. An example of such mediators is college completion, which explains later health disparities between Black women and White men. The goal is to quantify how much a disparity would be reduced (or remain) if we hypothetically intervened to set the mediator distribution equal across social groups. Despite increasing interest in estimating disparity reduction and the disparity that remains, various estimation procedures are not straightforward, and researchers have scant guidance for choosing an optimal method. In this article, the authors evaluate the performance in terms of bias, variance, and coverage of three approaches that use different modeling strategies: (1) regression-based methods that impose restrictive modeling assumptions (e.g., linearity) and (2) weighting-based and (3) imputation-based methods that rely on the observed distribution of variables. The authors find a trade-off between the modeling assumptions required in the method and its performance. In terms of performance, regression-based methods operate best as long as the restrictive assumption of linearity is met. Methods relying on mediator models without imposing any modeling assumptions are sensitive to the ratio of the group-mediator association to the mediator-outcome association. These results highlight the importance of selecting an appropriate estimation procedure considering the data at hand.

Exploring causal mechanisms and quantifying direct and indirect effects using a joint modeling approach for recurrent and terminal events.

Recurrent events are commonly encountered in biomedical studies. In many situations, there exist terminal events, such as death, which are potentially related to the recurrent events. Joint models of recurrent and terminal events have been proposed to address the correlation between recurrent events and terminal events. However, there is a dearth of suitable methods to rigorously investigate the causal mechanisms between specific exposures, recurrent events, and terminal events. For example, it is of interest to know how much of the total effect of the primary exposure of interest on the terminal event is through the recurrent events, and whether preventing recurrent event occurrences could lead to better overall survival. In this work, we propose a formal causal mediation analysis method to compute the natural direct and indirect effects. A novel joint modeling approach is used to take the recurrent event process as the mediator and the survival endpoint as the outcome. This new joint modeling approach allows us to relax the commonly used "sequential ignorability" assumption. Simulation studies show that our new model has good finite sample performance in estimating both model parameters and mediation effects. We apply our method to an AIDS study to evaluate how much of the comparative effectiveness of the two treatments and the effect of CD4 counts on the overall survival are mediated by recurrent opportunistic infections.

Investigation of Agricultural Sustainability with Irrigation and Economic Factors

Agricultural sustainability is becoming more and more important with the increasing world population. Therefore, the dissemination of sustainable agricultural practices; It is extremely important for future generations in terms of protecting the environment and natural resources, ensuring economic stability and increasing sustainable food production. This study investigates the sustainability in agriculture for Türkiye in terms of irrigation and economic factors in crop production change. Using the ARDL error correction model and Granger causality analysis methods for the period between 1995 and 2020, the short and long-term relationship between irrigation and economic factors and crop production value variables were analyzed. The research found that, the relationship between inflation variables irrigation, irrigation and the crop production value was found to be significant. In terms of agricultural sustainability, while the increase in the land opened to irrigation has led to an increase in crop production, the increase in the use of clean water from existing surface and groundwater resources has negatively affected the value of crop production. This has shown how important the water source and economic stability are in the sustainability of agriculture.