Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes.

In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the "gold-standard" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The "causal excursion effect," a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data.

Causal inference in the absence of positivity: The role of overlap weights.

How to analyze data when there is violation of the positivity assumption? Several possible solutions exist in the literature. In this paper, we consider propensity score (PS) methods that are commonly used in observational studies to assess causal treatment effects in the context where the positivity assumption is violated. We focus on and examine four specific alternative solutions to the inverse probability weighting (IPW) trimming and truncation: matching weight (MW), Shannon's entropy weight (EW), overlap weight (OW), and beta weight (BW) estimators. We first specify their target population, the population of patients for whom clinical equipoise, that is, where we have sufficient PS overlap. Then, we establish the nexus among the different corresponding weights (and estimators); this allows us to highlight the shared properties and theoretical implications of these estimators. Finally, we introduce their augmented estimators that take advantage of estimating both the propensity score and outcome regression models to enhance the treatment effect estimators in terms of bias and efficiency. We also elucidate the role of the OW estimator as the flagship of all these methods that target the overlap population. Our analytic results demonstrate that OW, MW, and EW are preferable to IPW and some cases of BW when there is a moderate or extreme (stochastic or structural) violation of the positivity assumption. We then evaluate, compare, and confirm the finite-sample performance of the aforementioned estimators via Monte Carlo simulations. Finally, we illustrate these methods using two real-world data examples marked by violations of the positivity assumption.

Genetic association and causal relationship between multiple modifiable risk factors and autoimmune liver disease: a two-sample mendelian randomization study.

BACKGROUND: The intricate etiology of autoimmune liver disease (AILD) involves genetic, environmental, and other factors that yet to be completely elucidated. This study comprehensively assessed the causal association between genetically predicted modifiable risk factors and AILD by employing Mendelian randomization. METHODS: Genetic variants associated with 29 exposure factors were obtained from genome-wide association studies (GWAS). Genetic association data with autoimmune hepatitis (AIH), primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC) were also obtained from publicly available GWAS. Univariate and multivariate Mendelian randomization analyses were performed to identify potential risk factors for AILD. RESULTS: Genetically predicted rheumatoid arthritis (RA) (OR = 1.620, 95%CI 1.423-1.843, P = 2.506 × 10- 13) was significantly associated with an increased risk of AIH. Genetically predicted smoking initiation (OR = 1.637, 95%CI 1.055-2.540, P = 0.028), lower coffee intake (OR = 0.359, 95%CI 0.131-0.985, P = 0.047), cholelithiasis (OR = 1.134, 95%CI 1.023-1.257, P = 0.017) and higher C-reactive protein (CRP) (OR = 1.397, 95%CI 1.094-1.784, P = 0.007) were suggestively associated with an increased risk of AIH. Genetically predicted inflammatory bowel disease (IBD) (OR = 1.212, 95%CI 1.127-1.303, P = 2.015 × 10- 7) and RA (OR = 1.417, 95%CI 1.193-1.683, P = 7.193 × 10- 5) were significantly associated with increased risk of PBC. Genetically predicted smoking initiation (OR = 1.167, 95%CI 1.005-1.355, P = 0.043), systemic lupus erythematosus (SLE) (OR = 1.086, 95%CI 1.017-1.160, P = 0.014) and higher CRP (OR = 1.199, 95%CI 1.019-1.410, P = 0.028) were suggestively associated with an increased risk of PBC. Higher vitamin D3 (OR = 0.741, 95%CI 0.560-0.980, P = 0.036) and calcium (OR = 0.834, 95%CI 0.699-0.995, P = 0.044) levels were suggestive protective factors for PBC. Genetically predicted smoking initiation (OR = 0.630, 95%CI 0.462-0.860, P = 0.004) was suggestively associated with a decreased risk of PSC. Genetically predicted IBD (OR = 1.252, 95%CI 1.164-1.346, P = 1.394 × 10- 9), RA (OR = 1.543, 95%CI 1.279-1.861, P = 5.728 × 10- 6) and lower glycosylated hemoglobin (HbA1c) (OR = 0.268, 95%CI 0.141-0.510, P = 6.172 × 10- 5) were positively associated with an increased risk of PSC. CONCLUSIONS: Evidence on the causal relationship between 29 genetically predicted modifiable risk factors and the risk of AIH, PBC, and PSC is provided by this study. These findings provide fresh perspectives on the management and prevention strategies for AILD.

Mendelian Randomization Analysis Reveals Causal Factors behind Alzheimer's Disease Risk: Evidence, Opportunities, and Challenges.

Alzheimer's disease and its comorbidities pose a heavy disease burden globally, and its treatment remains a major challenge. Identifying the protective and risk factors for Alzheimer's disease, as well as its possible underlying molecular processes, can facilitate the development of interventions that can slow its progression. Observational studies and randomized controlled trials have provided some evidence regarding potential risk factors for Alzheimer's disease; however, the results of these studies vary. Mendelian randomization is a novel epidemiological methodology primarily used to infer causal relationships between exposures and outcomes. Many Mendelian randomization studies have identified potential causal relationships between Alzheimer's disease and certain diseases, lifestyle habits, and biological exposures, thus providing valuable data for further mechanistic studies and the development and implementation of clinical prevention strategies. However, the results and data from Mendelian randomization studies must be interpreted based on comprehensive evidence. Moreover, the existing Mendelian randomization studies on the epidemiology of Alzheimer's disease have some limitations that are worth exploring. Therefore, the aim of this review was to summarize the available evidence on the potential protective and risk factors for Alzheimer's disease by assessing published Mendelian randomization studies on Alzheimer's disease, and to provide new perspectives on the etiology of Alzheimer's disease.

Causal inference in ethnographic research: Refining explanations with abductive logic, strength of evidence assessments, and graphical models.

In their classic accounts, anthropological ethnographers developed causal arguments for how specific sociocultural structures and processes shaped human thought, behavior, and experience in particular settings. Despite this history, many contemporary ethnographers avoid establishing in their work direct causal relationships between key variables in the way that, for example, quantitative research relying on experimental or longitudinal data might. As a result, ethnographers in anthropology and other fields have not advanced understandings of how to derive causal explanations from their data, which contrasts with a vibrant "causal revolution" unfolding in the broader social and behavioral sciences. Given this gap in understanding, we aim in the current article to clarify the potential ethnography has for illuminating causal processes related to the cultural influence on human knowledge and practice. We do so by drawing on our ongoing mixed methods ethnographic study of games, play, and avatar identities. In our ethnographic illustrations, we clarify points often left unsaid in both classic anthropological ethnographies and in more contemporary interdisciplinary theorizing on qualitative research methodologies. More specifically, we argue that for ethnographic studies to illuminate causal processes, it is helpful, first, to state the implicit strengths and logic of ethnography and, second, to connect ethnographic practice more fully to now well-developed interdisciplinary approaches to causal inference. In relation to the first point, we highlight the abductive inferential logic of ethnography. Regarding the second point, we connect the ethnographic logic of abduction to what Judea Pearl has called the ladder of causality, where moving from association to intervention to what he calls counterfactual reasoning produces stronger evidence for causal processes. Further, we show how graphical modeling approaches to causal explanation can help ethnographers clarify their thinking. Overall, we offer an alternative vision of ethnography, which contrasts, but nevertheless remains consistent with, currently more dominant interpretive approaches.

Causality between sleep traits and the risk of frailty: a Mendelian randomization study.

Background: Research based on observation has demonstrated a relationship between sleep traits and frailty; however, it remains uncertain if this correlation indicates causation. The purpose of this study was to look at the causal relationship that exists between frailty and sleep traits. Method: Using summaries from a genome-wide association study of self-reported sleep features and frailty index, we performed a bidirectional Mendelian randomization (MR) analysis. Examining the causal relationships between seven sleep-related traits and frailty was the goal. The major method used to calculate effect estimates was the inverse-variance weighted method, supplemented by the weighted median and MR-Egger approaches. The study investigated pleiotropy and heterogeneity using several methodologies, such as the MR-Egger intercept, the MR-PRESSO approach, and the Cochran's Q test. We took multivariate Mendelian randomization and genetic correlations between related traits to enhance the confidence of the results. Furthermore, we used MRlap to correct for any estimation bias due to sample overlap. Results: Insomnia, napping during the day, and sleep apnea syndrome exhibited a positive connection with the frailty index in forward MR analysis. Conversely, there is a negative link between getting up in the morning, snoring and sleep duration with the frailty index. During the reverse MR analysis, the frailty index exhibited a positive correlation with insomnia, napping during the day, and sleep apnea syndrome, while demonstrating a negative correlation with sleep duration. There was no direct correlation between snoring, chronotype, and frailty. In MVMR analyses, the causal effect of sleep characteristics on frailty indices remained consistent after adjusting for potential confounders including BMI, smoking, and triglycerides. Conclusion: The findings of our investigation yield novel evidence that substantiates the notion of a bidirectional causal connection between sleep traits and frailty. Through the optimization of sleep, it is potentially feasible to hinder, postpone, or even reverse the state of frailty, and we proposed relevant interventions.

Developing a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning.

BACKGROUND: Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only predict associations between variables, whereas causal graph learning models causality dynamics through graphs. However, building personalized causal graphs for each individual is challenging due to the limited amount of data available for each patient. METHOD: In this study, we present a new algorithmic framework using meta-learning for learning personalized causal graphs in biomedicine. Our framework extracts common patterns from multiple patient graphs and applies this information to develop individualized graphs. In multi-task causal graph learning, the proposed optimized initial guess of shared commonality enables the rapid adoption of knowledge to new tasks for efficient causal graph learning. RESULTS: Experiments on one real-world biomedical causal graph learning benchmark data and four synthetic benchmarks show that our algorithm outperformed the baseline methods. Our algorithm can better understand the underlying patterns in the data, leading to more accurate predictions of the causal graph. Specifically, we reduce the structural hamming distance by 50-75%, indicating an improvement in graph prediction accuracy. Additionally, the false discovery rate is decreased by 20-30%, demonstrating that our algorithm made fewer incorrect predictions compared to the baseline algorithms. CONCLUSION: To the best of our knowledge, this is the first study to demonstrate the effectiveness of meta-learning in personalized causal graph learning and cause inference modeling for biomedicine. In addition, the proposed algorithm can also be generalized to transnational research areas where integrated analysis is necessary for various distributions of datasets, including different clinical institutions.

Causal relationship between thyroid dysfunction and gastric cancer: a two-sample Mendelian randomization study.

Backgroud: Gastric cancer is one of the most common cancers worldwide, and its development is associated with a variety of factors. Previous observational studies have reported that thyroid dysfunction is associated with the development of gastric cancer. However, the exact relationship between the two is currently unclear. We used a two-sample Mendelian randomization (MR) study to reveal the causal relationship between thyroid dysfunction and gastric cancer for future clinical work. Materials and methods: This study is based on a two-sample Mendelian randomization design, and all data are from public GWAS databases. We selected hyperthyroidism, hypothyroidism, free thyroxine (FT4), and thyroid-stimulating hormone (TSH) as exposures, with gastric cancer as the outcome. We used three statistical methods, namely Inverse-variance weighted (IVW), MR-Egger, and weighted median, to assess the causal relationship between thyroid dysfunction and gastric cancer. The Cochran's Q test was used to assess the heterogeneity among SNPs in the IVW analysis results, and MR-PRESSO was employed to identify and remove IVs with heterogeneity from the analysis results. MR-Egger is a weighted linear regression model, and the magnitude of its intercept can be used to assess the horizontal pleiotropy among IVs. Finally, the data were visualized through the leave-one-out sensitivity test to evaluate the influence of individual SNPs on the overall causal effect. Funnel plots were used to assess the symmetry of the selected SNPs, forest plots were used to evaluate the confidence and heterogeneity of the incidental estimates, and scatter plots were used to assess the exposure-outcome relationship. All results were expressed as odds ratios (OR) and 95% confidence intervals (95% CI). P<0.05 represents statistical significance. Results: According to IVW analysis, there was a causal relationship between hypothyroidism and gastric cancer, and hypothyroidism could reduce the risk of gastric cancer (OR=0.936 (95% CI:0.893-0.980), P=0.006).This means that having hypothyroidism is a protective factor against stomach cancer. This finding suggests that hypothyroidism may be associated with a reduced risk of gastric cancer.Meanwhile, there was no causal relationship between hyperthyroidism, FT4, and TSH and gastric cancer. Conclusions: In this study, we found a causal relationship between hypothyroidism and gastric cancer with the help of a two-sample Mendelian randomisation study, and hypothyroidism may be associated with a reduced risk of gastric cancer, however, the exact mechanism is still unclear. This finding provides a new idea for the study of the etiology and pathogenesis of gastric cancer, and our results need to be further confirmed by more basic experiments in the future.

Causal relationship between rheumatoid arthritis and bronchiectasis: a bidirectional mendelian randomization study.

BACKGROUND: Epidemiological observational studies have elucidated a correlation between rheumatoid arthritis (RA) and bronchiectasis. However, the causal nature of this association remains ambiguous. To clarify this potential causal linkage, we conducted a two-sample Mendelian randomization (MR) analysis to explore the bidirectional causality between RA and bronchiectasis. METHODS: Summary statistics for RA and bronchiectasis were obtained from the IEU OpenGWAS database We employed various methods, including inverse variance weighting (IVW), MR-Egger, weighted median, weighted mode, and simple mode, to explore potential causal links between RA and bronchiectasis. Additionally, a series of sensitivity studies, such as Cochran's Q test, MR Egger intercept test, and leave-one-out analysis, were conducted to assess the MR analysis's accuracy further. RESULTS: In the forward MR analysis, the primary analysis indicated that a genetic predisposition to RA correlated with an increased risk of bronchiectasis in European populations (IVW odds ratio (OR): 1.28, 95% confidence interval (CI): 1.20-1.37, p = 1.18E-13). Comparable results were noted in the East Asian subjects (IVW OR: 1.55, 95% CI: 1.30-1.34, p = 8.33E-07). The OR estimates from the other four methods were consistent with those obtained from the IVW method. Sensitivity analysis detected no evidence of horizontal pleiotropy or heterogeneity. Conversely, in the reverse MR analysis, we found no evidence to support a genetic causality between bronchiectasis and RA in either European or East Asian populations. CONCLUSION: This study indicates that genetic predisposition to RA correlates with a heightened risk of bronchiectasis in both European and East Asian populations. These results imply that routine screening for bronchiectasis in RA patients could be beneficial, and effective management of RA may contribute to a reduced risk of bronchiectasis. Future research should aim to clarify the underlying mechanisms linking these two conditions.

Causal relationship between air pollution, lung function, gastroesophageal reflux disease, and non-alcoholic fatty liver disease: univariate and multivariate Mendelian randomization study.

Background: The association between air pollution, lung function, gastroesophageal reflux disease, and Non-alcoholic fatty liver disease (NAFLD) remains inconclusive. Previous studies were not convincing due to confounding factors and reverse causality. We aim to investigate the causal relationship between air pollution, lung function, gastroesophageal reflux disease, and NAFLD using Mendelian randomization analysis. Methods: In this study, univariate Mendelian randomization analysis was conducted first. Subsequently, Steiger testing was performed to exclude the possibility of reverse association. Finally, significant risk factors identified from the univariate Mendelian analysis, as well as important factors affecting NAFLD from previous observational studies (type 2 diabetes and body mass index), were included in the multivariable Mendelian randomization analysis. Results: The results of the univariable Mendelian randomization analysis showed a positive correlation between particulate matter 2.5, gastroesophageal reflux disease, and NAFLD. There was a negative correlation between forced expiratory volume in 1 s, forced vital capacity, and NAFLD. The multivariable Mendelian randomization analysis indicated a direct causal relationship between gastroesophageal reflux disease (OR = 1.537, p = 0.011), type 2 diabetes (OR = 1.261, p < 0.001), and NAFLD. Conclusion: This Mendelian randomization study confirmed the causal relationships between air pollution, lung function, gastroesophageal reflux, and NAFLD. Furthermore, gastroesophageal reflux and type 2 diabetes were identified as independent risk factors for NAFLD, having a direct causal connection with the occurrence of NAFLD.

Interventional probability of causation (IPoC) with epidemiological and partial mechanistic evidence: benzene vs. formaldehyde and acute myeloid leukemia (AML).

INTRODUCTION: Causal epidemiology for regulatory risk analysis seeks to evaluate how removing or reducing exposures would change disease occurrence rates. We define interventional probability of causation (IPoC) as the change in probability of a disease (or other harm) occurring over a lifetime or other specified time interval that would be caused by a specified change in exposure, as predicted by a fully specified causal model. We define the closely related concept of causal assigned share (CAS) as the predicted fraction of disease risk that would be removed or prevented by a specified reduction in exposure, holding other variables fixed. Traditional approaches used to evaluate the preventable risk implications of epidemiological associations, including population attributable fraction (PAF) and the Bradford Hill considerations, cannot reveal whether removing a risk factor would reduce disease incidence. We argue that modern formal causal models coupled with causal artificial intelligence (CAI) and realistically partial and imperfect knowledge of underlying disease mechanisms, show great promise for determining and quantifying IPoC and CAS for exposures and diseases of practical interest. METHODS: We briefly review key CAI concepts and terms and then apply them to define IPoC and CAS. We present steps to quantify IPoC using a fully specified causal Bayesian network (BN) model. Useful bounds for quantitative IPoC and CAS calculations are derived for a two-stage clonal expansion (TSCE) model for carcinogenesis and illustrated by applying them to benzene and formaldehyde based on available epidemiological and partial mechanistic evidence. RESULTS: Causal BN models for benzene and risk of acute myeloid leukemia (AML) incorporating mechanistic, toxicological and epidemiological findings show that prolonged high-intensity exposure to benzene can increase risk of AML (IPoC of up to 7e-5, CAS of up to 54%). By contrast, no causal pathway leading from formaldehyde exposure to increased risk of AML was identified, consistent with much previous mechanistic, toxicological and epidemiological evidence; therefore, the IPoC and CAS for formaldehyde-induced AML are likely to be zero. CONCLUSION: We conclude that the IPoC approach can differentiate between likely and unlikely causal factors and can provide useful upper bounds for IPoC and CAS for some exposures and diseases of practical importance. For causal factors, IPoC can help to estimate the quantitative impacts on health risks of reducing exposures, even in situations where mechanistic evidence is realistically incomplete and individual-level exposure-response parameters are uncertain. This illustrates the strength that can be gained for causal inference by using causal models to generate testable hypotheses and then obtaining toxicological data to test the hypotheses implied by the models-and, where necessary, refine the models. This virtuous cycle provides additional insight into causal determinations that may not be available from weight-of-evidence considerations alone.

Estimating causal effects from panel data with dynamic multivariate panel models.

Panel data are ubiquitous in scientific fields such as social sciences. Various modeling approaches have been presented for observational causal inference based on such data. Existing approaches typically impose restrictive assumptions on the data-generating process such as Gaussian responses or time-invariant effects, or they can only consider short-term causal effects. To surmount these restrictions, we present the dynamic multivariate panel model (DMPM) that supports time-varying, time-invariant, and individual-specific effects, multiple responses across a wide variety of distributions, and arbitrary dependency structures of lagged responses of any order. We formally demonstrate how DMPM facilitates causal inference within the structural causal modeling framework and we take a Bayesian approach for the estimation of the posterior distributions of the model parameters and causal effects of interest. We demonstrate the use of DMPM by applying the approach to both real and synthetic data.

Causal relationship between depression and hypercholesterolemia: A bidirectional 2-sample Mendelian randomization study.

Although observational studies have found both a positive and negative association between depression and hypercholesterolemia, the findings are mixed and contradictory. To our knowledge, this is the first study that employs the bidirectional Mendelian randomization (MR) and multivariable MR analysis with extensive genome-wide association studies (GWAS) data to examine the causal effect between depression and hypercholesterolemia. Using summary statistics obtained from GWAS of individuals with European ancestry, we utilize a bidirectional 2-sample MR approach to explore the potential causal association between hypercholesterolemia and depressive symptoms. Multivariable Mendelian randomization analysis was used to examine whether the direct causal effect of depression on the risk of hypercholesterolemia can be affected by traits associated with the increased risk of hypercholesterolemia. This MR analysis utilized inverse variance weighted (IVW), MR-Egger regression, weighted mode, and weighted median methods. Data on the summary level of depression were acquired from a GWAS that involved 500,199 participants. We used summary GWAS datasets for hypercholesterolemia including 206,067 participants. We also used another GWAS databases of hypercholesterolemiat (n = 463,010) to validate our results. By utilizing IVW, it was discovered that there is a possibility of a 31% rise in the risk of hypercholesterolemia due to depression (OR = 1.31, 95% CI = 1.10-1.57, P = .002). We found a consistent causal effect of depression on hypercholesterolemia from the IVW analyses using different hypercholesterolemia datasets. After adjustment of smoking, physical activity, and obesity, there remains significant causal relationship between depression and hypercholesterolemia (OR = 1.25, 95% CI = 1.01-1.54, P = .040). However, we did not find any evidence indicating that hypercholesterolemia leads to depression in the opposite direction. Directional pleiotropy was not observed in the MR-Egger regression analysis. Additionally, the MR-PRESSO analysis validated these discoveries. Neither the leave-one-out sensitivity test nor the funnel plots revealed any outliers. In both the unadjusted and adjusted estimates, depression has a consistent direct causal effect on hypercholesterolemia. Our study has led to an improved comprehension of the causal connections between hypercholesterolemia and depression, which could aid in the prevention and treatment of hypercholesterolemia.

Assessing Causal Relationships Between Periodontitis and Non-alcoholic Fatty Liver Disease: A Two-Sample Bidirectional Mendelian Randomisation Study.

PURPOSE: To investigate the causality between periodontitis and non-alcoholic fatty liver disease (NAFLD) using a two-sample bidirectional Mendelian randomisation (MR) analysis. MATERIALS AND METHODS: Genetic variations in periodontitis and NAFLD were acquired from genome-wide association studies (GWAS) using the Gene-Lifestyle Interaction in Dental Endpoints, a large-scale meta-analysis, and FinnGen consortia. Data from the first two databases were used to explore the causal relationship between periodontitis and NAFLD ("discovery stage"), and the data from FinnGen was used to validate our results ("validation stage"). We initially performed MR analysis using 5 single nucleotide polymorphisms (SNPs) in the discovery samples and 18 in the replicate samples as genetic instruments for periodontitis to investigate the causative impact of periodontitis on NAFLD. We then conducted a reverse MR analysis using 6 SNPs in the discovery samples and 4 in the replicate samples as genetic instruments for NAFLD to assess the causative impact of NAFLD on periodontitis. We further implemented heterogeneity and sensitivity analyses to assess the reliability of the MR results. RESULTS: Periodontitis was not causally related to NAFLD (odds ratio [OR] = 1.036, 95% CI: 0.914-1.175, p = 0.578 in the discovery stage; OR = 1.070, 95% CI: 0.935-1.224, p = 0.327 in the validation stage), and NAFLD was not causally linked with periodontitis (OR = 1.059, 95% CI: 0.916-1.225, p = 0.439 in the discovery stage; OR = 0.993, 95% CI: 0.896-1.102, p = 0.901 in the validation stage). No heterogeneity was observed among the selected SNPs. Sensitivity analyses demonstrated the absence of pleiotropy and the reliability of our MR results. CONCLUSION: The present MR analysis showed no genetic evidence for a cause-and-effect relationship between periodontitis and NAFLD. Periodontitis may not directly influence the development of NAFLD and vice versa.

Causal relationship between narcolepsy and anxiety: A two-sample Mendelian randomization study.

BACKGROUND: The aim of this study was to assess the causal relationship between narcolepsy and anxiety using Mendelian randomization (MR) methodology. METHODS: Our research applied a bidirectional two-sample Mendelian Randomization strategy to explore the linkage between narcolepsy and anxiety. Utilizing summary data from GWAS on both conditions, we primarily employed the inverse-variance weighted technique for our analysis. To evaluate heterogeneity and horizontal pleiotropy, we utilized tools such as the MR Egger method, the weighted median method, Cochran's Q statistic, and the MR Egger intercept. RESULTS: The analysis using the inverse variance-weighted method showed a clear positive link between narcolepsy and anxiety, with an odds ratio of 1.381 (95% CI: 1.161-1.642, p < 0.001). Tests for heterogeneity and horizontal pleiotropy, including MR Egger and IVW methods, indicated no significant findings (p-values 0.616 and 0.637, respectively, for heterogeneity; p = 0.463 for pleiotropy). Furthermore, no reverse causation was observed between anxiety and narcolepsy (odds ratio 1.034, 95% CI: 0.992-1.078, p = 0.111), with consistent findings across various analytical approaches. CONCLUSION: This research suggests a possible causal link between narcolepsy and anxiety disorders. The results illuminate this connection and advocate additional studies to elucidate the mechanisms involved and to identify effective interventions.

Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals.

Importance: Many medical journals, including JAMA, restrict the use of causal language to the reporting of randomized clinical trials. Although well-conducted randomized clinical trials remain the preferred approach for answering causal questions, methods for observational studies have advanced such that causal interpretations of the results of well-conducted observational studies may be possible when strong assumptions hold. Furthermore, observational studies may be the only practical source of information for answering some questions about the causal effects of medical or policy interventions, can support the study of interventions in populations and settings that reflect practice, and can help identify interventions for further experimental investigation. Identifying opportunities for the appropriate use of causal language when describing observational studies is important for communication in medical journals. Observations: A structured approach to whether and how causal language may be used when describing observational studies would enhance the communication of research goals, support the assessment of assumptions and design and analytic choices, and allow for more clear and accurate interpretation of results. Building on the extensive literature on causal inference across diverse disciplines, we suggest a framework for observational studies that aim to provide evidence about the causal effects of interventions based on 6 core questions: what is the causal question; what quantity would, if known, answer the causal question; what is the study design; what causal assumptions are being made; how can the observed data be used to answer the causal question in principle and in practice; and is a causal interpretation of the analyses tenable? Conclusions and Relevance: Adoption of the proposed framework to identify when causal interpretation is appropriate in observational studies promises to facilitate better communication between authors, reviewers, editors, and readers. Practical implementation will require cooperation between editors, authors, and reviewers to operationalize the framework and evaluate its effect on the reporting of empirical research.

Is there a causal relationship between autoimmune diseases and oropharyngeal cancer?

BACKGROUND: Autoimmune diseases (AIDs) are linked to oropharyngeal cancer (OPC), but the exact nature of this association remains unclear. This study aims to examine the potential causal effect of AIDs on the risk of developing OPC. METHOD: Information regarding AIDs was collected from the UK Biobank dataset and the Finn Gen study. OPC data were sourced from the IEU Open GWAS project. All data were derived from European populations. Inverse variance weighted (IVW) to two-sample Mendelian randomization (MR) was complemented by weighted median and MR Egger validation analyses. RESULT: The development of asthma (AS), multiple sclerosis (MS), and rheumatoid arthritis (RA) influenced the risk of developing OPC. However, the reverse MR analysis did not provide evidence for the impact of OPC on AIDs. Sensitivity analysis using MR corroborated the IVW results. The IVW results indicate OR values of 1.004 for AS, 0.936 for MS, and 1.0002 for RA. CONCLUSION: This MR study supports a causal relationship between asthma and rheumatoid arthritis for OPC in a European population. Multiple sclerosis was protective against OPC.

Causal inference for time-to-event data with a cured subpopulation.

When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.

Unraveling genetic causality between metformin and myocardial infarction on the basis of Mendelian randomization.

Background: In recent years, several studies have explored the effect of metformin on myocardial infarction (MI), but whether metformin has an improvement effect in patients with MI is controversial. This study was aimed to investigate the causal relationship between metformin and MI using Mendelian randomization (MR) analysis. Methods: The genome-wide significant (P<5×10-8) single-nucleotide polymorphisms (SNPs) in patients with metformin and patients with MI were screened from the Open genome-wide association study (GWAS) project as instrumental variables (IVs). The study outcomes mainly included MI, old MI, acute MI, acute transmural MI of inferior wall, and acute transmural MI of anterior wall. The inverse variance weighted (IVW) method was applied to assess the main causal effect, and weighted median, simple mode, weighted mode methods, and MR-Egger regression were auxiliary applied for supplementary proof. The causal relationship between metformin and MI was assessed using odds ratios (OR) and 95% confidence intervals (95% CI). A leave-one-out method was used to explore the effect of individual SNPs on the results of IVW analyses, and a funnel plot was used to analyze the potential bias of the study results, thus ensuring the robustness of the results. Results: In total, 16, 84, 39, 26, and 34 SNPs were selected as IVs to assess the genetic association between metformin and outcomes of MI, old MI, acute MI, acute transmural MI of inferior wall, and acute transmural MI of anterior wall, respectively. Treatment with metformin does not affect the risk of acute transmural MI of anterior wall at the genetic level (P>0.05; OR for inverse variance weighted was 1.010). In the cases of MI, old MI, acute MI, and acute transmural MI of inferior wall, metformin may even be a risk factor for patients (P<0.05; ORs for inverse variance weighted were 1.078, 1.026, 1.022 and 1.018 respectively). There was no horizontal pleiotropy or heterogeneity among IVs. The results were stable when removing the SNPs one by one. Conclusion: Metformin is not protective against the risk of myocardial infarction in patients and may even be a risk factor for MI, old MI, acute MI, and acute transmural MI of inferior wall.