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This paper presents a causal inference estimation method for longitudinal observational studies with multiple outcomes. The method uses marginal structural models with inverse probability treatment weights (MSM-IPTWs). In developing the proposed method, we re-define the weights as a product of inverse weights at each time point, accounting for time-varying confounders and treatment exposures and possible correlation between and within (serial) the multiple outcomes. The proposed method is evaluated by simulation studies and with an application to estimate the effect of HIV positivity awareness on condom use and multiple sexual partners using the Malawi Longitudinal Study of Families and Health (MLSFH) data. The simulation study shows that the joint MSM-IPTW performs well with coverage within the expected 95% level for a large sample size (n = 1000) and moderate to strong between and within outcome correlation strength ( $ \rho _j=0.3 $ ρj=0.3, 0.75, $ \rho _k=0.4 $ ρk=0.4, 0.8) when the effects are similar. The joint MSM-IPTW performed relatively the same as the adjusted standard joint model when the treatment effect estimate was the same for the outcomes. In the application, HIV positivity awareness increased the usage of condoms and did not affect the number of sexual partners. We recommend using the proposed MSM-IPTWs to correctly control for time-varying treatment and confounders when estimating causal effects for longitudinal observational studies with multiple outcomes. [ABSTRACT FROM AUTHOR]

Introducing a causal model to study the drought disaster around the world is the need of the planet today. There are many causes and effects of drought disasters around the world. These parameters, by affecting and being influenced by each other, cause the formation of processes that can make the experience of this event unpleasant. By planning and formulating appropriate strategies based on the results of this model, this event can be properly treated. For this purpose, in this research, the causal model of drought disasters is introduced. First, effective criteria or parameters in the occurrence of drought are identified. The tool used in this research is the modified fuzzy DEMATEL model. After responding to the direct relations matrix and defuzzification of this matrix, the causes and effects of the drought disaster are finally realized. The results of this study indicate that the criteria of global warming, mismanagement, and war are causal parameters or causes and the criteria of climate change, deforestation and soil degradation, dam construction, agriculture and livestock, and additional water needs are the parameters of the effect. Therefore, by establishing the conditions of each of the causal parameters, drought occurs or intensifies, and with the occurrence of drought, the effect parameters are created and show their effect on the causal parameters in the occurrence of drought. [ABSTRACT FROM AUTHOR]

Instrumental variables (IVs) methods have recently gained popularity since, under certain assumptions, they may yield consistent causal effect estimators in the presence of unmeasured confounding. Existing simulation studies that evaluate the performance of IV approaches for time-to-event outcomes tend to consider either an additive or a multiplicative data-generating mechanism (DGM) and have been limited to an exponential constant baseline hazard model. In particular, the relative merits of additive versus multiplicative IV models have not been fully explored. All IV methods produce less biased estimators than naïve estimators that ignore unmeasured confounding, unless the IV is very weak and there is very little unmeasured confounding. However, the mean squared error of IV estimators may be higher than that of the naïve, biased but more stable estimators, especially when the IV is weak, the sample size is small to moderate, and the unmeasured confounding is strong. In addition, the sensitivity of IV methods to departures from their assumed DGMs differ substantially. Additive IV methods yield clearly biased effect estimators under a multiplicative DGM whereas multiplicative approaches appear less sensitive. All can be extremely variable. We would recommend that survival probabilities should always be reported alongside the relevant hazard contrasts as these can be more reliable and circumvent some of the known issues with causal interpretation of hazard contrasts. In summary, both additive IV and Cox IV methods can perform well in some circumstances but an awareness of their limitations is required in analyses of real data where the true underlying DGM is unknown. [ABSTRACT FROM AUTHOR]

When and how does an environmental protest cycle affect election outcomes under electoral authoritarianism? Drawing on the case of the Bashkortostan republic, a Russian ethnic region, I leverage the spatial proximity to the protest site to identify its effects on parliamentary elections. The environmental protest cycle peaked in Bashkortostan around the regional government's decisions to extract minerals from shikhans – mountains composed of limestone. Employing a difference-in-differences (DiD) design, I show that precincts exposed to the environmental protest cycle experienced a significant drop in United Russia vote share, while voting for systemic opposition increased in the affected precincts. To explain the dynamics, I propose treating non-political protests, such as environmental ones, as an information revelation mechanism. The mechanism identified through a causal mediation analysis indicates that the environmental protest cycle conveys information on regional malperformance to voters. Their updated preferences, in turn, heavily undermine the electoral mobilizing capacity of local elites. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between annual disbursements of Brazil's largest development bank, BNDES, and mayors political affiliation. We explore a set of Difference-in-Difference (DiD) designs to evaluate causal effects of policy interventions. Using data of Brazilian municipalities, we find that municipalities with mayors belonging to the coalition that supports the Federal government get higher disbursements than the ones with mayors that are out of the coalition. There is strong evidence that firms located in allied municipalities receive average higher loans. Our findings support the view that political bias may distort the credit allocation of state-owned banks. [ABSTRACT FROM AUTHOR]

To assess the dynamic distributional impacts of macroeconomic policy, we propose quantile policy effects to quantify disparities between the quantiles of potential outcomes under different policies. We first identify quantile policy effects under the unconfoundedness assumption and propose an inverse probability weighting estimator. We then examine the asymptotic behavior of the proposed estimator in a time series framework and suggest a blockwise bootstrap method for inference. Applying this method, we investigate the effectiveness of U.S. macroprudential actions on bank credit growth from 1948 to 2019. Empirically, we find that the effects of macroprudential policy on credit growth are asymmetric and depend on the quantiles of credit growth. The tightening of macroprudential actions fails to rein in high credit growth, whereas easing policies do not effectively stimulate bank credit growth during low-growth periods. These findings suggest that U.S. macroprudential policies might not sufficiently address the challenges of soaring bank credit or ensure overarching financial stability. [ABSTRACT FROM AUTHOR]

This paper examines the causal effects of disaster relief on employment using municipality level data. To do this, we focus on the disaster relief for the Great East Japan Earthquake. Our empirical results show that while simple difference-in-differences estimation does not necessarily show statistically significant effects, the estimation results using some matching techniques are robust. Our results suggest that natural disaster relief could contribute to employment recovery in afflicted areas, contrary to some theoretical assumptions that fiscal expansion depresses private-sector employment. [ABSTRACT FROM AUTHOR]

ABSTRACT Gait visual scores are widely applied to horse breeding because they are a fast and easy phenotyping strategy, allowing the numeric interpretation of a complex biological process such as gait quality. However, they may suffer from subjectivity or high environmental influence. We aimed to investigate potential causal relationships among six visual gait scores in Campolina horses. The data included 5475 horses with records for at least one of the following traits: Dissociation (Di), Comfort (C), Style (S), Regularity (R), Development (De), and Gait total Scores (GtS). The pedigree comprised three generations with 14,079 horses in the additive relationship matrix. Under a Bayesian framework, (co)variance components were estimated through a multitrait animal model (MTM). Then, the inductive causation algorithm (IC) was applied to the residual (co)variance matrix samples. The resulting undirected graph from IC was directed in 6 possible causal structures, each fitted by a structural equation model. The final causal structure was chosen based on deviance information criteria (DIC). It was found that S significantly impacts the causal network of gait, directly and indirectly affecting C. The indirect causal effect of S on C was through the direct effect of S on De, then the direct effect of De on R, and finally, the direct effect of R on C. Di was caused by S, which is the reason for the genetic correlation between Di and GtS, due to causal effects being added to the model, they absorb the genetic correlation between Di and GtS. Those paths have biological meaning to horse movements and can help breeders and researchers better understand horses' complex causal network of gait. [ABSTRACT FROM AUTHOR]

We study the identification of causal effects, motivated by two improvements to identifiability that can be attained if one knows that some variables in a causal graph are functionally determined by their parents (without needing to know the specific functions). First, an unidentifiable causal effect may become identifiable when certain variables are functional. Secondly, certain functional variables can be excluded from being observed without affecting the identifiability of a causal effect, which may significantly reduce the number of needed variables in observational data. Our results are largely based on an elimination procedure that removes functional variables from a causal graph while preserving key properties in the resulting causal graph, including the identifiability of causal effects. Our treatment of functional dependencies in this context mandates a formal, systematic, and general treatment of positivity assumptions, which are prevalent in the literature on causal effect identifiability and which interact with functional dependencies, leading to another contribution of the presented work. [ABSTRACT FROM AUTHOR]

Background: Previous articles on the relationship between vitamin D and gestational diabetes mellitus (GDM) were inconsistent. Their relationship has been observed primarily through observational studies, and the causality of this association has not been established. Methods: A two-sample Mendelian randomization (MR) research was conducted to test the causal association between vitamin D and GDM, utilizing publically available statistics from genome-wide association studies (GWAS). This study obtained genetic variants from GWAS including vitamin D (N = 373,045,10,783,672 Single Nucleotide Polymorphisms SNPs), and GDM (5687 cases and 117,892 controls). The major technique was the inverse variance weighted approach (IVW), although there were other approaches as well, such as MR-Egger regression, weighted median, weighted mode, and simple mode. Additionally, we conducted sensitivity analyses to detect any potential diversity and horizontal pleiotropy. Results: The study suggested that there was no causal link between vitamin D and GDM (all methods p > 0.05). For heterogeneity, MR egger Q value was 113.7, p < 0.05; IVW Q value was 114.7, p < 0.05. Therefore, random- effects IVW approach was applied. Regarding pleiotropy, the MR Egger regression intercept was 0.0046, which was close to zero with a p value of 0.452, suggesting the absence of pleiotropy. Conclusions: We observed no assosiation between genetically predicted vitamin D and the risk of GDM, implying that insufficient vitamin D may do not confer an increased susceptibility to GDM. [ABSTRACT FROM AUTHOR]

Distracted driving is a threat to traffic safety that can result in more traffic crashes. Although previous studies have been conducted to explore the relationship between driver distraction and hazardous driving actions, few studies are available to identify the causation between them. Thus, the study intended to evaluate the causal effects of distraction on hazardous driving actions at intersections based on the crash data extracted from the Crash Report Sampling System (2021–2022). The multinomial logit model was employed to reveal the factors contributing to driver distraction. Then, propensity score weighting was adopted to balance the factor distributions between distraction and non-distraction cases to identify the causal effects on hazardous actions. Results indicated that 1) the propensity of distraction is relevant to factors such as the driver's age, gender, vehicle type, speed limit, area, weather, and light condition, 2) driver distraction can significantly increase the probability of risky actions including speeding, running red lights, failing to obey stop signs, failing to yield, following too closely, and 3) the causal effects show great diversity for different distraction types. The findings serve to understand the influence mechanism of distraction on specific crash risks and develop countermeasures to reduce distraction and hazardous driving actions. [ABSTRACT FROM AUTHOR]

In this study, we examine complementarities in usage across a set of related software products from a multi-product firm. We employ a novel experimental approach to causally estimate complementarities, leveraging rich usage data and advertising experiments that directly affect the usage of only one product at a time to measure complementarities based on consumption rather than purchase. Our approach is particularly useful as digital contexts are characterized by the simultaneous presence of both substitutability and complementarity between products. They also have scant price variation, bundled pricing plans, and infrequent purchase or subscription renewal decisions, often making typical cross-price elasticity measures for complementarities infeasible. We apply our approach to data from a software company with a suite of related products and find evidence for varying degrees of complementarity across both user groups and products. We show that accounting for complementarities significantly affects the measurement of ad effectiveness and may impact ad targeting decisions by the firm. We explore heterogeneity in complementarities, finding that they are larger for users who have used the products heavily in the past, but small or zero for those who have not. Ours is one of the first studies to causally examine complementarity in usage in the context of subscription products, and our identification strategy can be applied to a variety of contexts. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to investigate the causal effects of physical activity, sedentary behaviour, and diet on atrial fibrillation (AF) and heart failure (HF) using multivariate Mendelian randomization (MR) analysis and genetic variants as instrumental variables. Methods: The study employed multivariate MR analysis with physical activity, sedentary behaviour, and diet as exposures and AF and HF as outcomes. Data were obtained from the UK Biobank (over 500,000 participants) and the FinnGen project (218,792 participants of European ancestry). Genetic variants associated with physical activity, diet, and sedentary behaviour were used as instrumental variables. The main analysis methods included the inverse variance weighted (IVW) method, MR-Egger, and weighted median methods. Heterogeneity was assessed using Cochran's Q test. Results: The analyses generally did not demonstrate significant causal relationships between physical activity or sedentary behaviour and AF. Diet showed a potential protective effect on AF in some analyses but was not consistently significant across methods. For HF, physical activity and sedentary behaviour did not show significant causal relationships. Diet showed a significant protective effect against HF in the IVW method but was not consistent across all methods. Conclusions: This study suggests that while there may be some protective effects of these lifestyle factors on cardiovascular disease, most analyses did not show significant causality, and results were inconsistent. Further research is needed to validate these findings. [ABSTRACT FROM AUTHOR]

Abstract Numerous observational studies suggest associations between proton pump inhibitors (PPIs) and dementia, but causal relationships remain uncertain. Using large-scale genome-wide association study (GWAS) data, we performed univariable Mendelian randomization (UVMR) analysis to assess the causality between five PPI types, and all-cause dementia and its five subtypes. Confounders were controlled through multivariable MR (MVMR) analysis to isolate PPIs’ direct effects on dementia. Heterogeneity and pleiotropy assessments, and leave-one-out analysis, were conducted to validate the robustness of our results. Initial UVMR estimates suggested that lansoprazole (odds ratio [OR] 1.291; 95%confidence interval [CI] 1.001–1.665; p = 0.049) and pantoprazole (OR 1.118; 95% CI 1.014–1.233; p = 0.025) potentially increased VD risk, with their respective direct associations also discovered in MVMR. Additionally, FTD was found to reversely increase rabeprazole use (OR 1.086; 95% CI 1.011–1.167; p = 0.023). However, after adjustment for the false discovery rate (FDR), none of these associations remained statistically significant (pFDR> 0.05). The robustness of our results is supported by consistent estimates across complementary MR methods and the absence of pleiotropy. Our study indicates no robust causality between PPI use and increased dementia risk. Thus, it is inappropriate to restrict clinically justified PPI prescriptions merely due to potential cognitive risks.

Abstract Previous research has suggested that circulating inflammatory proteins are associated with benign prostatic disease (BPD). This Mendelian randomization (MR) study was conducted to further investigate the causal relationship between 91 inflammatory proteins and BPD. Genome-wide association study (GWAS) summarized data of benign prostatic hyperplasia (BPH) and prostatitis were obtained from the FinnGen Biobank. The latest study offered the GWAS data on 91 proteins related to inflammation. We performed a bidirectional MR to investigate the causal association between inflammatory proteins and BPD. The outcomes of the IVW method indicated that decreased levels of circulating interleukin-17 C (IL-17 C) (OR = 0.92, 95%CI = 0.85–0.99, p-value = 0.0344) were suggestively associated with a higher risk of BPH and elevated levels of interleukin-10 receptor subunit alpha (IL-10RA) (OR = 1.24, 95%CI = 1.05–1.47, p-value = 0.0132) and urokinase-type plasminogen activator (uPA) (OR = 1.13, 95%CI = 1.00–1.28, p-value = 0.0421) were suggestively related to a higher risk of prostatitis. Furthermore, reverse MR revealed that BPH may promote the expression of circulating factors, including natural killer cell receptor 2B4 (CD244) (OR = 1.07, 95%CI = 1.01–1.13, p-value = 0.0192), T-cell surface glycoprotein CD6 isoform (CD6) (OR = 1.07, 95%CI = 1.01–1.13, p-value = 0.0192), and leukemia inhibitory factor receptor (LIF-R) (OR = 1.07, 95%CI = 1.01–1.15, p-value = 0.0163). Moreover, the results of sensitivity analyses indicate that heterogeneity and horizontal pleiotropy are unlikely to distort the findings. The results of this study indicate a potential association between circulating inflammatory proteins and BPD, which may become new diagnostic indicators or drug targets for clinical application in the prevention and treatment of BPD. However, further investigation is required.

Abstract Background Causal mediation analysis is widespread in applied medical research, especially in longitudinal settings. However, estimating natural mediational effects in such contexts is often difficult because of the presence of post-treatment confounding. Moreover, many models frequently used in applied research, like multilevel and latent growth models, present an additional difficulty, i.e. the presence of latent variables. In this paper, we propose a causal interpretation of these two classes of models based on a novel type of causal effects called separable, which overcome some of the issues of natural effects. Methods We formally derive conditions for the identifiability of separable mediational effects and their analytical expressions based on the g-formula. We carry out a simulation study to investigate how moderate and severe model misspecification, as well as violation of the identfiability assumptions, affect estimates. We also present an application to real data. Results The results show how model misspecification impacts the estimates of mediational effects, particularly in the case of severe misspecification, and that the bias worsens over time. The violation of assumptions affects separable effect estimates in a very different way for the mixed effect and the latent growth models. Conclusion Our approach allows us to give multilevel and latent growth models an appealing causal interpretation based on separable effects. The simulation study shows that model misspecification can heavily impact effect estimates, highlighting the importance of careful model choice.

Abstract Background Esophageal diseases (ED) are a kind of common diseases of upper digestive tract. Previous studies have proved that metabolic disorders are closely related to the occurrence and development of ED. However, there is a lack of evidence for causal relationships between metabolites and ED, as well as between metabolite ratios representing enzyme activities and ED. Herein, we explored the causality of genetically determined metabolites (GDMs) on ED through Mendelian Randomization (MR) study. Methods Two-sample Mendelian randomization analysis was used to assess the causal effects of genetically determined metabolites and metabolite ratios on ED. A genome-wide association analysis (GWAS) encompassing 850 individual metabolites along with 309 metabolite ratios served as the exposures. Meanwhile, the outcomes were defined by 10 types of ED phenotypes, including Congenital Malformations of Esophagus (CME), Esophageal Varices (EV), Esophageal Obstructions (EO), Esophageal Ulcers (EU), Esophageal Perforations (EP), Gastroesophageal Reflux Disease (GERD), Esophagitis, Barrett's Esophagus (BE), Benign Esophageal Tumors (BETs), and Malignant Esophageal Neoplasms (MENs). The standard inverse variance weighted (IVW) method was applied to estimate the causal relationship between exposure and outcome. Sensitivity analyses were carried out using multiple methods, including MR-Egger, Weighted Median, MR-PRESSO, Cochran's Q test, and leave-one-out analysis. P < 0.05 was conventionally considered statistically significant. After applying the Bonferroni correction for multiple testing, a threshold of P < 4.3E-05 (0.05/1159) was regarded as indicative of a statistically significant causal relationship. Furthermore, metabolic pathway analysis was performed using the web-based MetaboAnalyst 6.0 software. Results The findings revealed that initially, a total of 869 candidate causal association pairs ( $${P}_{ivw}$$ P ivw < 0.05) were identified, involving 442 metabolites, 145 metabolite ratios and 10 types of ED. However, upon applying the Bonferroni correction for multiple testing, only 36 pairs remained significant, involving 28 metabolites (predominantly lipids and amino acids), 5 metabolite ratios and 6 types of ED. Sensitivity analyses and reverse MR were performed for these 36 causal association pairs, where the results showed that the pair of EV and 1-(1-enyl-palmitoyl)-2-linoleoyl-GPE (p-16:0/18:2) did not withstand the sensitivity tests, and Hexadecenedioate (C16:1-DC) was found to have a reverse causality with GERD. The final 34 robust causal pairs included 26 metabolites, 5 metabolite ratios and 5 types of ED. The involved 26 metabolites predominantly consisted of methylated nucleotides, glycine derivatives, sex hormones, phospholipids, bile acids, fatty acid dicarboxylic acid derivatives, and N-acetylated amino acids. Furthermore, through metabolic pathway analysis, we uncovered 8 significant pathways that played pivotal roles in five types of ED conditions. Conclusions This study integrated genomics with metabolomics to assess causal relationships between ED and both metabolites and metabolite ratios, uncovering several key metabolic features in ED pathogenesis. These findings have potential as novel biomarkers for ED and provide insights into the disease's etiology and progression. However, further clinical and experimental validations are necessary

The chief aim of this research is to investigate the causality of air pollutants and adverse pregnancy outcomes. Two-sample Mendelian randomization was conducted, employing genetic variants connected with air pollution as instrumental variables. Sixteen adverse pregnancy outcomes were extracted as the main outcome measures from the genome-wide association study (GWAS). The inverse-variance weighted (IVW) method was conducted as the primary analysis method. This study found that there were causal association between NO2 and pre-eclampsia (weighted median: OR = 1.30, 95% CI = [1.03–1.64], p = 0.029) and between PM2.5 and placental abruption (IVW: OR = 10.94, 95% CI = [1.28–93.45], p = 0.029). There were potential causal relationships between NO2 and gestational hypertension (IVW: OR = 1.14, 95% CI = [0.99–1.30], p = 0.060); NO2 and placental abruption (IVW: OR = 1.97, 95% CI = [0.90–4.28], p = 0.089); NOx and fetal growth restriction (IVW: OR = 0.06, 95% CI = [0.99–1.12], p = 0.089); PM2.5 and slow fetal growth and fetal malnutrition (MR–Egger: OR = 54,240.95, 95% CI = [2.08–1,411,757,729.46], p = 0.059); PM10 and hyperemesis gravidarum (MR–Egger: OR = 0.12, 95% CI = [0.02–0.97], p = 0.086); PM10 and preterm birth (weighted median: OR = 1.60, 95% CI = [0.95–2.70], p = 0.075); and PM10 and spontaneous abortion (weighted median: OR = 1.60, 95% CI = [0.95–2.70], p = 0.075). There was no pleiotropy, but there was some heterogeneity. In conclusion, air pollution has a causal effect on several adverse pregnancy outcomes. [ABSTRACT FROM AUTHOR]

Machine learning techniques for causal effect estimation can enhance the reliability of epidemiologic analyses, reducing their dependence on correct model specifications. However, the stochastic nature of many machine learning algorithms implies that the results derived from such approaches may be influenced by the random seed that is set before model fitting. In this work, we highlight the substantial influence of random seeds on a popular approach for machine learning-based causal effect estimation, namely doubly robust estimators. We illustrate that varying seeds can yield divergent scientific interpretations of doubly robust estimates produced from the same dataset. We propose techniques for stabilizing results across random seeds and, through an extensive simulation study, demonstrate that these techniques effectively neutralize seed-related variability without compromising the statistical efficiency of the estimators. Based on these findings, we offer practical guidelines to minimize the influence of random seeds in real-world applications, and we encourage researchers to explore the variability due to random seeds when implementing any method that involves random steps. [ABSTRACT FROM AUTHOR]

We propose a new estimator for average causal effects of a binary treatment with panel data in settings with general treatment patterns. Our approach augments the popular two‐way‐fixed‐effects specification with unit‐specific weights that arise from a model for the assignment mechanism. We show how to construct these weights in various settings, including the staggered adoption setting, where units opt into the treatment sequentially but permanently. The resulting estimator converges to an average (over units and time) treatment effect under the correct specification of the assignment model, even if the fixed‐ effect model is misspecified. We show that our estimator is more robust than the conventional two‐way estimator: it remains consistent if either the assignment mechanism or the two‐way regression model is correctly specified. In addition, the proposed estimator performs better than the two‐way‐fixed‐effect estimator if the outcome model and assignment mechanism are locally misspecified. This strong robustness property underlines and quantifies the benefits of modeling the assignment process and motivates using our estimator in practice. We also discuss an extension of our estimator to handle dynamic treatment effects. [ABSTRACT FROM AUTHOR]

Background: Causal mediation analysis is widespread in applied medical research, especially in longitudinal settings. However, estimating natural mediational effects in such contexts is often difficult because of the presence of post-treatment confounding. Moreover, many models frequently used in applied research, like multilevel and latent growth models, present an additional difficulty, i.e. the presence of latent variables. In this paper, we propose a causal interpretation of these two classes of models based on a novel type of causal effects called separable, which overcome some of the issues of natural effects. Methods: We formally derive conditions for the identifiability of separable mediational effects and their analytical expressions based on the g-formula. We carry out a simulation study to investigate how moderate and severe model misspecification, as well as violation of the identfiability assumptions, affect estimates. We also present an application to real data. Results: The results show how model misspecification impacts the estimates of mediational effects, particularly in the case of severe misspecification, and that the bias worsens over time. The violation of assumptions affects separable effect estimates in a very different way for the mixed effect and the latent growth models. Conclusion: Our approach allows us to give multilevel and latent growth models an appealing causal interpretation based on separable effects. The simulation study shows that model misspecification can heavily impact effect estimates, highlighting the importance of careful model choice. [ABSTRACT FROM AUTHOR]

Previous research has suggested that circulating inflammatory proteins are associated with benign prostatic disease (BPD). This Mendelian randomization (MR) study was conducted to further investigate the causal relationship between 91 inflammatory proteins and BPD. Genome-wide association study (GWAS) summarized data of benign prostatic hyperplasia (BPH) and prostatitis were obtained from the FinnGen Biobank. The latest study offered the GWAS data on 91 proteins related to inflammation. We performed a bidirectional MR to investigate the causal association between inflammatory proteins and BPD. The outcomes of the IVW method indicated that decreased levels of circulating interleukin-17 C (IL-17 C) (OR = 0.92, 95%CI = 0.85–0.99, p-value = 0.0344) were suggestively associated with a higher risk of BPH and elevated levels of interleukin-10 receptor subunit alpha (IL-10RA) (OR = 1.24, 95%CI = 1.05–1.47, p-value = 0.0132) and urokinase-type plasminogen activator (uPA) (OR = 1.13, 95%CI = 1.00–1.28, p-value = 0.0421) were suggestively related to a higher risk of prostatitis. Furthermore, reverse MR revealed that BPH may promote the expression of circulating factors, including natural killer cell receptor 2B4 (CD244) (OR = 1.07, 95%CI = 1.01–1.13, p-value = 0.0192), T-cell surface glycoprotein CD6 isoform (CD6) (OR = 1.07, 95%CI = 1.01–1.13, p-value = 0.0192), and leukemia inhibitory factor receptor (LIF-R) (OR = 1.07, 95%CI = 1.01–1.15, p-value = 0.0163). Moreover, the results of sensitivity analyses indicate that heterogeneity and horizontal pleiotropy are unlikely to distort the findings. The results of this study indicate a potential association between circulating inflammatory proteins and BPD, which may become new diagnostic indicators or drug targets for clinical application in the prevention and treatment of BPD. However, further investigation is required. [ABSTRACT FROM AUTHOR]

We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity. We consider a general model with group-level unconfoundedness and provide conditions under which aggregate balancing statistics—group-level averages of functions of treatments and covariates—are sufficient to eliminate differences between groups. Building on these results, we re-interpret commonly used linear fixed-effect regression estimators by writing them in the Mundlak form as linear regression estimators without fixed effects but including group averages. We use this representation to develop Generalized Mundlak Estimators that capture group differences through group averages of (functions of) the unit-level variables and adjust for these group differences in flexible and robust ways in the spirit of the modern causal literature. [ABSTRACT FROM AUTHOR]

Clinical trials with random assignment of treatment provide evidence about causal effects of an experimental treatment compared to standard care. However, when disease processes involve multiple types of possibly semi-competing events, specification of target estimands and causal inferences can be challenging. Intercurrent events such as study withdrawal, the introduction of rescue medication, and death further complicate matters. There has been much discussion about these issues in recent years, but guidance remains ambiguous. Some recommended approaches are formulated in terms of hypothetical settings that have little bearing in the real world. We discuss issues in formulating estimands, beginning with intercurrent events in the context of a linear model and then move on to more complex disease history processes amenable to multistate modeling. We elucidate the meaning of estimands implicit in some recommended approaches for dealing with intercurrent events and highlight the disconnect between estimands formulated in terms of potential outcomes and the real world. [ABSTRACT FROM AUTHOR]

Background: Esophageal diseases (ED) are a kind of common diseases of upper digestive tract. Previous studies have proved that metabolic disorders are closely related to the occurrence and development of ED. However, there is a lack of evidence for causal relationships between metabolites and ED, as well as between metabolite ratios representing enzyme activities and ED. Herein, we explored the causality of genetically determined metabolites (GDMs) on ED through Mendelian Randomization (MR) study. Methods: Two-sample Mendelian randomization analysis was used to assess the causal effects of genetically determined metabolites and metabolite ratios on ED. A genome-wide association analysis (GWAS) encompassing 850 individual metabolites along with 309 metabolite ratios served as the exposures. Meanwhile, the outcomes were defined by 10 types of ED phenotypes, including Congenital Malformations of Esophagus (CME), Esophageal Varices (EV), Esophageal Obstructions (EO), Esophageal Ulcers (EU), Esophageal Perforations (EP), Gastroesophageal Reflux Disease (GERD), Esophagitis, Barrett's Esophagus (BE), Benign Esophageal Tumors (BETs), and Malignant Esophageal Neoplasms (MENs). The standard inverse variance weighted (IVW) method was applied to estimate the causal relationship between exposure and outcome. Sensitivity analyses were carried out using multiple methods, including MR-Egger, Weighted Median, MR-PRESSO, Cochran's Q test, and leave-one-out analysis. P < 0.05 was conventionally considered statistically significant. After applying the Bonferroni correction for multiple testing, a threshold of P < 4.3E-05 (0.05/1159) was regarded as indicative of a statistically significant causal relationship. Furthermore, metabolic pathway analysis was performed using the web-based MetaboAnalyst 6.0 software. Results: The findings revealed that initially, a total of 869 candidate causal association pairs ( P ivw < 0.05) were identified, involving 442 metabolites, 145 metabolite ratios and 10 types of ED. However, upon applying the Bonferroni correction for multiple testing, only 36 pairs remained significant, involving 28 metabolites (predominantly lipids and amino acids), 5 metabolite ratios and 6 types of ED. Sensitivity analyses and reverse MR were performed for these 36 causal association pairs, where the results showed that the pair of EV and 1-(1-enyl-palmitoyl)-2-linoleoyl-GPE (p-16:0/18:2) did not withstand the sensitivity tests, and Hexadecenedioate (C16:1-DC) was found to have a reverse causality with GERD. The final 34 robust causal pairs included 26 metabolites, 5 metabolite ratios and 5 types of ED. The involved 26 metabolites predominantly consisted of methylated nucleotides, glycine derivatives, sex hormones, phospholipids, bile acids, fatty acid dicarboxylic acid derivatives, and N-acetylated amino acids. Furthermore, through metabolic pathway analysis, we uncovered 8 significant pathways that played pivotal roles in five types of ED conditions. Conclusions: This study integrated genomics with metabolomics to assess causal relationships between ED and both metabolites and metabolite ratios, uncovering several key metabolic features in ED pathogenesis. These findings have potential as novel biomarkers for ED and provide insights into the disease's etiology and progression. However, further clinical and experimental validations are necessary [ABSTRACT FROM AUTHOR]

Previous studies on exposure to violence lack a nuanced understanding of the causal effects of different exposure types on offending behaviors. This study, drawing on Pathways to Desistance Study (PDS) data tracking 1354 adjudicated youths aged 14–18 over 7 years, explores the contemporaneous (cross‐sectional), acute (after 1 year), enduring (after 3 years), and long‐term (after 6 years) causal effects of violence exposure on property and violent offending. The sample, predominantly male (86%), consisted of White (20%), Black (42%), and other (38%) individuals. The generalized propensity score is used to match unbalanced covariates across multiple exposure types, namely noninvolved (n = 392), witnessed (n = 577), experienced (n = 31), and experienced‐witnessed violence (n = 305). Results demonstrate the contemporaneous, acute, enduring, and long‐term effects of violence exposure on both violent and property offending, with varying durations and strengths across exposure types. The most pronounced risk effects are immediate, diminishing over time and potentially reversing in the long term as youth transition into adulthood. Among exposure types, experienced‐witnessed violence exhibits the most potent effects on offending, followed by witnessed violence and then experienced violence—a pattern consistent across the observed time points. Noteworthy is the finding that the impact of violence exposure is more pronounced for violent offending, diminishing more rapidly compared to the effects on property offending. [ABSTRACT FROM AUTHOR]

In this survey we discuss the recent causal panel data literature. This recent literature has focused on credibly estimating causal effects of binary interventions in settings with longitudinal data, emphasising practical advice for empirical researchers. It pays particular attention to heterogeneity in the causal effects, often in situations where few units are treated and with particular structures on the assignment pattern. The literature has extended earlier work on difference-in-differences or two-way fixed effect estimators. It has more generally incorporated factor models or interactive fixed effects. It has also developed novel methods using synthetic control approaches. [ABSTRACT FROM AUTHOR]

Background The prevalence of allergic diseases in China has risen significantly over the past decades, affecting the quality of life for approximately 40% of the population. Objectives This study aimed to integrate survey and genomic data to explore the potential causal relationship between alcohol consumption and allergic diseases. Method In collaboration with a leading genetic testing company in China, we collected data on 3,041 participants via an online survey between December 2018 and October 2019. A Mendelian Randomization (MR) design was employed in data analysis, leveraging the random allocation of genes at meiosis in humans to create instrumental variables for alcohol intake. This method was used to estimate the causal effect of alcohol consumption on the incidence of allergic diseases. Results While ordinary least-squares estimates showed a negative association between alcohol drinking and the risk of self-reported allergic diseases, MR estimates suggest that higher alcohol consumption increased the risks of allergy in certain subgroups. Specifically, predicted drinking [b = 0.445, p = 0.032] and the number of drinking times during the past 30 days [b = 0.031, p

Studies of colorism—the idea that racial hierarchies coexist with gradational inequalities based on skin color—consistently find that darker skin correlates with lower socioeconomic outcomes. Despite the causal nature of this debate, evidence remains predominantly associational. This study revisits the colorism literature by proposing a causal model underlying these theories. It discusses conditions under which associations may reflect contemporary causal effects of skin color and evaluates strategies for identifying these effects. Using data from the AddHealth and NLSY97 surveys and applying two identification strategies, the study estimates the causal effects of skin color on college degree attainment, personal earnings, and family income among White, Black, and Hispanic populations in the United States. Results show that darker skin correlates with poorer educational and economic outcomes within racial groups. However, evidence of contemporary causal effects of skin color is partial, limited to college attainment of Whites and family income of Hispanics. For Blacks, results suggest a generalized penalty associated with being Black rather than gradation based on skin tone. Methodologically, the article advocates using sensitivity analyses to account for unobserved confounders in models for skin color effects and uses sibling fixed-effects as a secondary complementary strategy.

Abstract Cervical cancer (CC) is a prevalent gynecological cancer worldwide that significantly impacts the quality of life and the physical and mental well-being of women. However, there have been limited studies utilizing Mendelian randomization (MR) analysis to investigate the connection between immune cells and CC. This study is to investigate the causal effects of immune traits on CC and non-neoplastic conditions of the cervix. The GWAS data for 731 immunophenotypes and six GWAS data for CC from the FinnGen database were downloaded. Subsequently, a two-sample MR analysis was conducted using the MR Egger, Weighted median, Inverse variance weighted (IVW), Simple mode, and Weighted mode methods. Our study has identified the potential causal effects of immune traits on inflammatory diseases of the cervix, other noninflammatory disorders of the cervix uteri, carcinoma in situ of cervix uteri, adenocarcinomas of cervix, squamous cell neoplasms and carcinoma of cervix, as well as malignant neoplasm of the cervix uteri, with the respective numbers being 8, 6, 11, 8, 23, and 12, respectively. A strong correlation between classic monocytes and various cervical diseases was revealed. Furthermore, we discovered that B cells expressing BAFF-R have the ability to impede the advancement of malignant CC, specifically squamous cell neoplasms and carcinoma of cervix. Our study has demonstrated a significant association between immune traits and both CC and non-neoplastic conditions of the cervix through two-sample Mendelian randomization, providing valuable insights for future clinical research.

This research is framed in behavioral economics. This area tests the orthodox assumptions that individuals are rational, self-interested and possess all freely available information, and. Behavioral economics plays an important role for policymakers in areas such as environmental protection. We observe that despite being very concerned about environmental problems, the reality is that a great heterogeneity of behaviors is observed. Faced with the same level of concern, some citizens act coherently by adopting pro-environmental behaviors, while others do not. This latter response is supposed to generate cognitive dissonance. Accordingly, we expect that the levels of pro-environmental behavior should be more in line with observed levels of concern. Understanding pro-environmental behavior (PEB) is still a challenge. Insight into causal mechanisms of environmental concern on PEB could shed light on the effectiveness of environmental strategies such as land management, recycling, environmental taxes, water quality, human health, and prevention of further biodiversity loss. We employ a structural equation model to identify mechanisms through which environmental concern affects PEB. We prove that causal mechanisms between environmental concern dimensions, i.e., environmental concern in a broad sense, such as affection, cognitive, conative and active-are not independent. Additionally, we demonstrate that the average indirect effect (ACME), the average direct effect (ADE) and the average total effect (TE) of environmental concern on pro-environmental behavior depend on the baseline status of environmental concern in a narrow sense, i.e., worry or affection for environmental protection. The magnitude of the effects is also moderated by situational factors such as income, age, education, household size, and municipality size. This psychological construct (environmental concern) allows us to better understand the observed heterogeneity related to PEB which affects the economic efficiency of political measures. [ABSTRACT FROM AUTHOR]

The synthetic control method (SCM) represents a notable innovation in estimating the causal effects of policy interventions and programs in a comparative case study setting. In this paper, we demonstrate that the data-driven approach to SCM requires solving a bilevel optimization problem. We show how the original SCM problem can be solved to the global optimum through the introduction of an iterative algorithm rooted in Tykhonov regularization or Karush–Kuhn–Tucker approximations. [ABSTRACT FROM AUTHOR]

As disasters increase due to climate change, population density, epidemics, and technology, information is needed about postdisaster consequences for people's mental health and how stress-related mental disorders affect multiple spheres of life, including labor-market attachment. We tested the causal hypothesis that individuals who developed stress-related mental disorders as a consequence of their disaster exposure experienced subsequent weak labor-market attachment and poor work-related outcomes. We leveraged a natural experiment in an instrumental variables model, studying a 2004 fireworks factory explosion disaster that precipitated the onset of stress-related disorders (posttraumatic stress disorder, anxiety, and depression) among individuals in the local community (N = 86,726). We measured labor-market outcomes using longitudinal population-level administrative data: sick leave, unemployment benefits, early retirement pension, and income from wages from 2007 to 2010. We found that individuals who developed a stress-related disorder after the disaster were likely to go on sickness benefit, both in the short-and long-term, were likely to use unemployment benefits and to lose wage income in the long term. Stress-related disorders did not increase the likelihood of early retirement. The natural experiment design minimized the possibility that omitted confounders biased these effects of mental health on work outcomes. Addressing the mental health and employment needs of survivors after a traumatic experience may improve their labor-market outcomes and their nations' economic outputs. [ABSTRACT FROM AUTHOR]

Cervical cancer (CC) is a prevalent gynecological cancer worldwide that significantly impacts the quality of life and the physical and mental well-being of women. However, there have been limited studies utilizing Mendelian randomization (MR) analysis to investigate the connection between immune cells and CC. This study is to investigate the causal effects of immune traits on CC and non-neoplastic conditions of the cervix. The GWAS data for 731 immunophenotypes and six GWAS data for CC from the FinnGen database were downloaded. Subsequently, a two-sample MR analysis was conducted using the MR Egger, Weighted median, Inverse variance weighted (IVW), Simple mode, and Weighted mode methods. Our study has identified the potential causal effects of immune traits on inflammatory diseases of the cervix, other noninflammatory disorders of the cervix uteri, carcinoma in situ of cervix uteri, adenocarcinomas of cervix, squamous cell neoplasms and carcinoma of cervix, as well as malignant neoplasm of the cervix uteri, with the respective numbers being 8, 6, 11, 8, 23, and 12, respectively. A strong correlation between classic monocytes and various cervical diseases was revealed. Furthermore, we discovered that B cells expressing BAFF-R have the ability to impede the advancement of malignant CC, specifically squamous cell neoplasms and carcinoma of cervix. Our study has demonstrated a significant association between immune traits and both CC and non-neoplastic conditions of the cervix through two-sample Mendelian randomization, providing valuable insights for future clinical research. [ABSTRACT FROM AUTHOR]

Studies of colorism-the idea that racial hierarchies coexist with gradational inequalities based on skin color-consistently find that darker skin correlates with lower socioeconomic outcomes. Despite the causal nature of this debate, evidence remains predominantly associational. This study revisits the colorism literature by proposing a causal model underlying these theories. It discusses conditions under which associations may reflect contemporary causal effects of skin color and evaluates strategies for identifying these effects. Using data from the AddHealth and NLSY97 surveys and applying two identification strategies, the study estimates the causal effects of skin color on college degree attainment, personal earnings, and family income among White, Black, and Hispanic populations in the United States. Results show that darker skin correlates with poorer educational and economic outcomes within racial groups. However, evidence of contemporary causal effects of skin color is partial, limited to college attainment of Whites and family income of Hispanics. For Blacks, results suggest a generalized penalty associated with being Black rather than gradation based on skin tone. Methodologically, the article advocates using sensitivity analyses to account for unobserved confounders in models for skin color effects and uses sibling fixed-effects as a secondary complementary strategy. [ABSTRACT FROM AUTHOR]

BackgroundsGrowing evidence has indicated that the nutritional quality of dietary intake and alterations in blood metabolites were related to human brain activity. This study aims to investigate the causal relationship between dietary component intake, blood metabolites, and delirium risks.MethodsWe performed Mendelian randomization (MR) analysis using genetic variants as instrumental variables for dietary component intake, blood metabolites, and delirium. Inverse variance weighting, maximum likelihood, weighted median, weighted mode, and MR-Egger methods were used for statistical analyses.ResultsWe found that genetic prediction of salt added to food (odds ratio [OR] 1.715, 95% confidence interval [CI] 1.239–2.374, p = 0.001) significantly increased the risks of delirium, while low-fat polyunsaturated margarine used in cooking (OR 0.044, 95%CI 0.004–0.432, p = 0.007), cheese intake (OR 0.691, 95%CI 0.500–0.955, p = 0.025) and coffee intake (OR 0.595, 95%CI 0.370–0.956, p = 0.032) was suggestively associated with decreased risks of delirium. Moreover, increased blood 1-stearoylglycerol levels (OR 0.187, 95%CI 0.080–0.435, p = 9.97E-05) significantly contributed to reducing the risks of delirium. 3-methoxytyrosine (OR 0.359, 95%CI 0.154–0.841, p = 0.018) also has the potential to decrease the risk of delirium.ConclusionOur study highlights the potential causal effect relationships of dietary component intake and blood metabolites on the risk of delirium, which potentially provides novel insights into targeted dietary prevention strategies or biomarkers for delirium.

Abstract Background Type 1 diabetes mellitus (T1DM) has been associated with higher pulmonary tuberculosis (PTB) risk in observational studies. However, the causal relationship between them remains unclear. This study aimed to assess the causal effect between T1DM and PTB using bidirectional Mendelian randomization (MR) analysis. Methods Single nucleotide polymorphisms (SNPs) of T1DM and PTB were extracted from the public genetic variation summary database. In addition, GWAS data were collected to explore the causal relationship between PTB and relevant clinical traits of T1DM, including glycemic traits, lipids, and obesity. The inverse variance weighting method (IVW), weighted median method, and MR‒Egger regression were used to evaluate the causal relationship. To ensure the stability of the results, sensitivity analyses assess the robustness of the results by estimating heterogeneity and pleiotropy. Results IVW showed that T1DM increased the risk of PTB (OR = 1.07, 95% CI: 1.03–1.12, P

Background: Primary ovarian insufficiency (POI) is a common clinical endocrine disorder with a high heterogeneity in both endocrine hormones and etiological phenotypes. However, the etiology of POI remains unclear. Herein, we unraveled the causality of genetically determined metabolites (GDMs) on POI through Mendelian randomization (MR) study with the overarching goal of disclosing underlying mechanisms. Methods: Genetic links with 486 metabolites were retrieved from GWAS data of 7824 European participants as exposures, while GWAS data concerning POI were utilized as the outcome. Via MR analysis, we selected inverse-variance weighted (IVW) method for primary analysis and several additional MR methods (MR-Egger, weighted median, and MR-PRESSO) for sensitivity analyses. MR-Egger intercept and Cochran's Q statistical analysis were conducted to assess potential heterogeneity and pleiotropy. In addition, genetic variations in the key target metabolite were scrutinized further. We conducted replication, meta-analysis, and linkage disequilibrium score regression (LDSC) to reinforce our findings. The MR Steiger test and reverse MR analysis were utilized to assess the robustness of genetic directionality. Furthermore, to deeply explore causality, we performed colocalization analysis and metabolic pathway analysis. Results: Via IVW methods, our study identified 33 metabolites that might exert a causal effect on POI development. X-11437 showed a robustly significant relationship with POI in four MR analysis methods (P IVW=0.0119; P weightedmedian =0.0145; PMR-Egger =0.0499; PMR-PRESSO =0.0248). Among the identified metabolites, N-acetylalanine emerged as the most significant in the primary MR analysis using IVW method, reinforcing its pivotal status as a serum biomarker indicative of an elevated POI risk with the most notable P-value (P IVW=0.0007; PMR-PRESSO =0.0022). Multiple analyses were implemented to further demonstrate the reliability and stability of our deduction of causality. Reverse MR analysis did not provide evidence for the causal effects of POI on 33 metabolites. Colocalization analysis revealed that some causal associations between metabolites and POI might be driven by shared genetic variants. Conclusion: By incorporating genomics with metabolomics, this study sought to offer a comprehensive analysis in causal impact of serum metabolome phenotypes on risks of POI with implications for underlying mechanisms, disease screening and prevention. [ABSTRACT FROM AUTHOR]

Background: There is growing evidence of associations between the gut microbiota and anxiety disorders, where changes in gut microbiotas may affect brain function and behavior via the microbiota-gut-brain axis. However, population-level studies offering a higher level of evidence for causality are lacking. Our aim was to investigate the specific gut microbiota and associated metabolites that are closely related to anxiety disorders to provide mechanistic insights and novel management perspectives for anxiety disorders. Method: This study used summary-level data from publicly available Genome-Wide Association Studies (GWAS) for 119 bacterial genera and the phenotype "All anxiety disorders" to reveal the causal effects of gut microbiota on anxiety disorders and identify specific bacterial genera associated with anxiety disorders. A two-sample, bidirectional Mendelian randomization (MR) design was deployed, followed by comprehensive sensitivity analyses to validate the robustness of results. We further conducted multivariable MR (MVMR) analysis to investigate the potential impact of neurotransmitter-associated metabolites, bacteriaassociated dietary patterns, drug use or alcohol consumption, and lifestyle factors such as smoking and physical activity on the observed associations. Results: Bidirectional MR analysis identified three bacterial genera causally related to anxiety disorders: the genus Eubacterium nodatum group and genus Ruminococcaceae UCG011 were protective, while the genus Ruminococcaceae UCG011 was associated with an increased risk of anxiety disorders. Further MVMR suggested that a metabolite-dependent mechanism, primarily driven by tryptophan, tyrosine, phenylalanine, glycine and cortisol, which is consistent with previous research findings, probably played a significant role in mediating the effects of these bacterial genera to anxiety disorders. Furthermore, modifying dietary pattern such as salt, sugar and processed meat intake, and adjusting smoking state and physical activity levels, appears to be the effective approaches for targeting specific gut microbiota to manage anxiety disorders. Conclusion: Our findings offer potential avenues for developing precise and effective management approaches for anxiety disorders by targeting specific gut microbiota and associated metabolites. [ABSTRACT FROM AUTHOR]

The avoidance of causality in the design, analysis and interpretation of non-experimental studies has often been criticised as an untenable scientific stance, because theories are based on causal relations (and not associations) and a rich set of methodological tools for causal analysis has been developed in recent decades. Psychology researchers (n = 106 with complete data) participated in an online study presenting a causal statement about the results of a fictitious paper on the potential effect of drinking clear water for years on the risk of dementia. Two randomised groups of participants were then asked to reflect on the conflict between the goal of approaching a causal answer and the prevailing norm of avoiding doing so. One of the two groups was also instructed to think about possible benefits of addressing causality. Both groups then responded to a list of 19 items about attitudes to causal questions in science. A control group did this without reflecting on conflict or benefits. Free-text assessments were also collected during reflection, giving some indication of how and why causality is avoided. We condense the exploratory findings of this study into five new hypotheses about the how and why, filtered through what can be explained by cognitive dissonance reduction theory. These concern the cost of addressing causality, the variety of ways in which dissonance can be reduced, the need for profound intervention through teaching and social aspects. Predictions are derived from the hypotheses for confirmation trials in future studies and recommendations for teaching causality. Open data are provided for researchers' own analyses. [ABSTRACT FROM AUTHOR]

Instead of usingmanifest proxies for a latent outcome or latent covariates in a causal effect analysis, the R package EffectLiteR facilitates a direct integration of latent variables based on structural equation models (SEM). The corresponding framework considers latent interactions and provides various effect estimates for evaluating the differential effectiveness of treatments. In addition, a user-friendly graphical interface customizes the implementation of the complex models. We aim to enable applications of EffectLiteR in more contexts, and therefore generalize the framework for incorporating latent variables measured with categorical indicators. This refers, for instance, to achievement tests in educational large-scale assessments (LSAs), which are typically constructed in the tradition of item response theory (IRT). We review different modeling strategies for incorporating latent variables from IRT models in an effect analysis (i.e., individual score estimates, plausible values, SEM for categorical indicators). The strategies differ in the handling of measurement error and, thus, have different implications for the accuracy and efficiency of causal effect estimates. We describe our extensions of EffectLiteR based on SEM for categorical indicators and illustrate the model specification step-bystep. In addition, we present a hands-on example, where we apply EffectLiteR in LSA data. The practical benefit of using latent variables in comparison to proficiency scores is of special interest in the application and discussion. When assessing psychological constructs, like competencies, preferences, personality attributes, and so forth, measurement error typically occurs in the observed scores. Latent variable models can control for the measurement error, but do not provide estimates of the person's true scores on the latent construct. Thus, for using the latent variables in subsequent analysis, the analysis method has to be combined somehow with the latent variable model. One option is the implementation of two-step procedures that obtain score estimates from latent variable models, which are then used as manifest variables in the analysis model. Yet, score estimates are not always error-free. As an alternative the R package EffectLiteR directly incorporates latent variable models for estimating various covariate adjusted effects in nonrandomized group comparisons. We extend this approach for latent covariates and latent outcome variables that are modeled in the tradition of item response theory with categorical indicators. For implementing the complexmodels (i.e., amoderated regression with latent variables that is implemented as amultidimensionalmultigroup structural equationmodel for ordered categorical indicators), we describe the EffectLiteR syntax and the model specification through a graphical user interface. In addition, we regard the benefit of latent variables for causal effect estimation in comparison to using score estimates (i.e., individual score estimates or plausible values). For this we review the assumptions of the different analysis strategies and present a hands-on example with large scale assessment data. [ABSTRACT FROM AUTHOR]

Uric acid, the end-product of purine metabolism within the human body, has been the subject of studies exploring its potential association with cardiovascular and cerebrovascular diseases. However, the precise relationship between uric acid levels and heart failure remains elusive. In this particular study, aggregated data from genome-wide association studies on uric acid and heart failure were utilized to perform a two-sample Mendelian randomization (MR) analysis utilizing R software. The aim was to uncover any causal link between these variables. The primary outcome was assessed using inverse variance weighted (IVW) methodology, while sensitivity analyses employed MR-Egger, weighted median (WME), and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) techniques. IVW results revealed a possible causal relationship between elevated uric acid levels and an increased risk of heart failure (OR: 1.09, 95 % CI: 1.01–1.17, P < 0.05). Encouragingly, the directions provided by MR-Egger and WME aligned with IVW findings, and no anomalies were detected in the remaining sensitivity analyses. These outcomes indicate the stability of the results of the study, thereby suggesting that heightened uric acid levels may contribute to an augmented risk of heart failure. • It's the first MR study to evaluate the causal relationship between uric acid levels and heart failure. • MR study avoids the influence of confounding factors from the perspective of genetic variation. • The study has confirmed a causal relationship between uric acid levels and heart failure. [ABSTRACT FROM AUTHOR]

Since their introduction by Abadie and Gardeazabal, Synthetic Control (SC) methods have quickly become one of the leading methods for estimating causal effects in observational studies in settings with panel data. Formal discussions often motivate SC methods by the assumption that the potential outcomes were generated by a factor model. Here we study SC methods from a design-based perspective, assuming a model for the selection of the treated unit(s) and period(s). We show that the standard SC estimator is generally biased under random assignment. We propose a Modified Unbiased Synthetic Control (MUSC) estimator that guarantees unbiasedness under random assignment and derive its exact, randomization-based, finite-sample variance. We also propose an unbiased estimator for this variance. We document in settings with real data that under random assignment, SC-type estimators can have root mean-squared errors that are substantially lower than that of other common estimators. We show that such an improvement is weakly guaranteed if the treated period is similar to the other periods, for example, if the treated period was randomly selected. While our results only directly apply in settings where treatment is assigned randomly, we believe that they can complement model-based approaches even for observational studies. [ABSTRACT FROM AUTHOR]

Background: Type 1 diabetes mellitus (T1DM) has been associated with higher pulmonary tuberculosis (PTB) risk in observational studies. However, the causal relationship between them remains unclear. This study aimed to assess the causal effect between T1DM and PTB using bidirectional Mendelian randomization (MR) analysis. Methods: Single nucleotide polymorphisms (SNPs) of T1DM and PTB were extracted from the public genetic variation summary database. In addition, GWAS data were collected to explore the causal relationship between PTB and relevant clinical traits of T1DM, including glycemic traits, lipids, and obesity. The inverse variance weighting method (IVW), weighted median method, and MR‒Egger regression were used to evaluate the causal relationship. To ensure the stability of the results, sensitivity analyses assess the robustness of the results by estimating heterogeneity and pleiotropy. Results: IVW showed that T1DM increased the risk of PTB (OR = 1.07, 95% CI: 1.03–1.12, P < 0.001), which was similar to the results of MR‒Egger and weighted median analyses. Moreover, we found that high-density lipoprotein cholesterol (HDL-C; OR = 1.28, 95% CI: 1.03–1.59, P = 0.026) was associated with PTB. There was no evidence of an effect of glycemic traits, remaining lipid markers, or obesity on the risk of PTB. In the reverse MR analysis, no causal relationships were detected for PTB on T1DM and its relevant clinical traits. Conclusion: This study supported that T1DM and HDL-C were risk factors for PTB. This implies the effective role of treating T1DM and managing HDL-C in reducing the risk of PTB, which provides an essential basis for the prevention and comanagement of concurrent T1DM and PTB in clinical practice. [ABSTRACT FROM AUTHOR]

Previous epidemiological evidence suggests rheumatoid arthritis is associated with sarcopenia-related features. However, most of the current evidence is from cross-sectional studies, and the causal link of this association is still to be determined. Therefore, this study was committed to a two-sample Mendelian randomization analysis to assess the causal effect of rheumatoid arthritis on sarcopenia-related features. In this two-sample Mendelian randomization study, instrumental variables for rheumatoid arthritis were obtained from the Non-Cancer Disease Study, and data for the five relevant characteristics of sarcopenia were pooled from UKBiobank. Inverse variance weighting is the primary analysis method for assessing causal effects. MR-Egger regression and weighted median are complementary analysis methods for causal effects. Leave-one-out analysis, horizontal pleiotropy test, and Heterogeneity test are applied as a sensitivity analysis to assess the robustness of causal effect estimates. The inverse variance weighted results for the five characteristics associated with sarcopenia and rheumatoid arthritis were: hand grip strength (right) (beta = − 2.309, se = 0.206, p = 3.340E-29), hand grip strength (left) (beta = − 2.046, se = 0.205, p = 2.166E-23), whole body lean mass (beta = − 0.843, se = 0.135, p = 4.67E-10), appendicular lean mass (beta = − 2.444, se = 0.208, p = 6.069E-32), Usual walking pace (OR 0.340, 95% CI (0.238, 0.484), p = 2.471E-09). The sensitivity analyses did not support that horizontal pleiotropy distorted causal effect estimates. The beta coefficient quantifies the number of standard deviations of the continuous outcome variables (hand grip strength, whole body lean mass, and appendicular lean mass) that change on average with each increase in the standard deviation of the binary exposure variable (rheumatoid arthritis). The odds ratios indicate the increased risk of the binary outcome variable (usual walking pace) per rheumatoid arthritis standard deviation increase. This study has demonstrated a negative causal effect of rheumatoid arthritis with five major sarcopenia-related features in a European population. [ABSTRACT FROM AUTHOR]