A marginal structural model for normal tissue complication probability.

The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summarized as dose-volume histograms (DVHs). Normal tissue complication probability (NTCP) modeling has centered around making patient-level risk predictions with features extracted from the DVHs, but few have considered adapting a causal framework to evaluate the safety of alternative treatment plans. We propose causal estimands for NTCP based on deterministic and stochastic interventions, as well as propose estimators based on marginal structural models that impose bivariable monotonicity between dose, volume, and toxicity risk. The properties of these estimators are studied through simulations, and their use is illustrated in the context of radiotherapy treatment of anal canal cancer patients.

Vaping Transitions and Incident Depressive Symptoms among Young Adults: A Marginal Structural Model Analysis.

The extent to which vaping influences depression is unclear, but could be estimated through application of novel epidemiologic methods. Among a prospective cohort of young adults from California who screened negative for depression, we estimated repeated measures marginal structural models to examine the association of four vaping transitions from time T to T+1 (persistent use, discontinuation, initiation, persistent nonuse) with risk of clinically significant depressive symptoms at T+1, simultaneously across three ~1.5 year time-intervals between 2017-2021. Stabilized inverse probability of treatment and censoring weights adjusted for time-dependent confounders and selection bias. Among n=3,496 observations (1,806 participants, mean pooled baseline age=19.5), 8.1% reported persistent vaping from T to T+1, 6.2% reported discontinuation (i.e., use at T and no use at T+1), 6.5% initiated e-cigarettes (i.e., no use at T and use at T+1), and 79.2% reported persistent nonuse at both time-points. Compared to persistent vaping at two waves, persistent nonuse (RR=0.76, 95%CI:0.62-0.93) and discontinuation (RR=0.71, 95%CI:0.52-0.96) were associated with lower risk of depression. Associations were robust to sensitivity analyses, including restricting to tobacco naïve participants and varying temporal assumptions to reduce potential for reverse causation. Young adults who consistently avoid or discontinue vaping may be protected from depressive symptom occurrence.

Evaluating the Effectiveness of Systemic Heparin During Arteriovenous Fistula Creation by Emulating a Target Trial.

Most of the 800,000 people living with end-stage kidney disease in the United States rely on a functioning vascular access to provide life-sustaining hemodialysis, yet one-third of arteriovenous fistulas experience early failures. Determining the safety and effectiveness of systemic heparin during fistula creation could improve the quality and quantity of life for these vulnerable patients. In this paper, a pragmatic randomized trial was emulated to assess the effect of systemic heparin administration (vs. none) during radiocephalic arteriovenous fistula creation on early bleeding and thrombosis using data from two international multicenter randomized trials performed between 2014 and 2019. Marginal risks were estimated using inverse probability weighted parametric survival analysis and confidence intervals were generated with bootstrapping. A total of 914 patients were enrolled and 61% received systemic heparin; median (IQR) age was 58 (49, 67) years and 45% were on hemodialysis at enrollment. No difference in the risk of bleeding events was observed, with a risk difference (95% CI) at 14 days of -0.1% (-1.6, 1.4). The risk of access thrombosis was lower in the heparin group, with a risk of 3.7% (2.6, 4.8) after heparin and 5.3% (3.4, 7.4) without heparin at 14 days (risk ratio 0.72, 95% CI 0.50, 0.98).

Semiparametric Bayesian inference for optimal dynamic treatment regimes via dynamic marginal structural models.

Considerable statistical work done on dynamic treatment regimes (DTRs) is in the frequentist paradigm, but Bayesian methods may have much to offer in this setting as they allow for the appropriate representation and propagation of uncertainty, including at the individual level. In this work, we extend the use of recently developed Bayesian methods for Marginal Structural Models to arrive at inference of DTRs. We do this (i) by linking the observational world with a world in which all patients are randomized to a DTR, thereby allowing for causal inference and then (ii) by maximizing a posterior predictive utility, where the posterior distribution has been obtained from nonparametric prior assumptions on the observational world data-generating process. Our approach relies on Bayesian semiparametric inference, where inference about a finite-dimensional parameter is made all while working within an infinite-dimensional space of distributions. We further study Bayesian inference of DTRs in the double robust setting by using posterior predictive inference and the nonparametric Bayesian bootstrap. The proposed methods allow for uncertainty quantification at the individual level, thereby enabling personalized decision-making. We examine the performance of these methods via simulation and demonstrate their utility by exploring whether to adapt HIV therapy to a measure of patients' liver health, in order to minimize liver scarring.

Time-Varying Proteinuria and Progression of IgA Nephropathy: A Cohort Study.

RATIONALE & OBJECTIVE: Proteinuria is a surrogate end point for predicting long-term kidney outcomes in IgA nephropathy (IgAN) with levels<1g/day identified as a therapeutic target. However, this threshold has not been sufficiently studied. We quantified the associations of progression of IgAN with various levels of proteinuria. STUDY DESIGN: Observational cohort study. SETTING & PARTICIPANTS: 1,530 patients with IgAN and at least 12 months of follow-up at Peking University First Hospital. EXPOSURE: Proteinuria levels updated over time (time-varying proteinuria, TVP). OUTCOME: A composite kidney outcome of a 50% reduction in the estimated glomerular filtration rate or end-stage kidney disease. ANALYTICAL APPROACH: Marginal structural models. RESULTS: After a median follow-up period of 43.5 (IQR, 27.2-72.8) months, 254 patients (16.6%) developed the composite kidney outcome. A graded association was observed between TVP and composite kidney outcomes with higher risk among those with proteinuria of≥0.5g/day. Compared with TVP<0.3g/day, the HRs for proteinuria levels of 0.3 to<0.5g/day, 0.5 to<1.0g/day, 1.0 to<2.0g/day, and≥2.0g/day were 2.22 (95% CI, 0.88-5.58), 4.04 (95% CI, 1.93-8.46), 8.46 (95% CI, 3.80-18.83), and 38.00 (95% CI, 17.62-81.95), respectively. The trend was more pronounced in patients with baseline proteinuria of≥1.0g/day, among whom a higher risk was observed with TVP of 0.3 to<0.5g/day compared with TVP<0.3g/day (HR, 3.26 [95% CI, 1.07-9.92], P=0.04). However, in patients with baseline proteinuria levels of<1g/day, the risk of composite kidney outcome only began to increase when TVP was≥1.0g/day (HR, 3.25 [95% CI, 1.06-9.90]). LIMITATIONS: Single-center observational study, selection bias, and unmeasured confounders. CONCLUSIONS: This study showed that patients with IgAN and proteinuria levels of>0.5g/day, have an elevated risk of kidney failure especially among patients with proteinuria levels≥1.0g/day before initiating treatment. These data may serve to inform the selection of proteinuria targets in the treatment of IgAN. PLAIN-LANGUAGE SUMMARY: The presence of proteinuria has often been considered a surrogate end point and a possible therapeutic target in clinical trials in IgA nephropathy (IgAN). Some guidelines recommend a reduction in proteinuria to<1g/day as a treatment goal based on the results of previous longitudinal studies. However, these findings may have been biased because they did not properly adjust for time-dependent confounders. Using marginal structural models to appropriately account for these confounding influences, we observed that patients with IgAN and proteinuria levels≥0.5g/day have an elevated risk of kidney failure, especially among patients who had proteinuria levels of≥1.0g/day before initiating treatment. These data may serve to inform the selection of proteinuria targets in the treatment of IgAN.

Self-Reported PrEP Use and Risk of Bacterial STIs Among Ontarian Men Who Are Gay or Bisexual or Have Sex With Men.

PURPOSE: HIV pre-exposure prophylaxis (PrEP) may increase rates of bacterial sexually transmitted infections (STIs) among gay, bisexual, and other men who have sex with men (GBM) through risk compensation (eg, an increase in condomless sex or number of partners); however, longitudinal studies exploring the time-dependent nature of PrEP uptake and bacterial STIs are limited. We used marginal structural models to estimate the effect of PrEP uptake on STI incidence. METHODS: We analyzed data from the iCruise study, an online longitudinal study of 535 Ontarian GBM from July 2017 to April 2018, to estimate the effects of PrEP uptake on incidence of self-reported bacterial STIs (chlamydia, gonorrhea, and syphilis) collected with 12 weekly diaries. The incidence rate was calculated as the number of infections per 100 person-months, with evaluation of the STIs overall and individually. We used marginal structural models to account for time-varying confounding and quantitative bias analysis to evaluate the sensitivity of estimates to nondifferential outcome misclassification. RESULTS: Participating GBM were followed up for a total of 1,623.5 person-months. Overall, 70 participants (13.1%) took PrEP during the study period. Relative to no uptake, PrEP uptake was associated with an increased incidence rate of gonorrhea (incidence rate ratio = 4.00; 95% CI, 1.67-9.58), but not of chlamydia or syphilis, and not of any bacterial STI overall. Accounting for misclassification, the median incidence rate ratio for gonorrhea was 2.36 (95% simulation interval, 1.08-5.06). CONCLUSIONS: We observed an increased incidence rate of gonorrhea associated with PrEP uptake among Ontarian GBM that was robust to misclassification. Although our findings support current guidelines for integrating gonorrhea screening with PrEP services, additional research should consider the long-term impact of PrEP among this population.Annals Early Access article.

Effect of cytomegalovirus infection on post-transplant hospitalization days among children undergoing allogeneic hematopoietic cell transplantation: A marginal structural model approach.

BACKGROUND: Cytomegalovirus (CMV) commonly reactivates after allogeneic hematopoietic cell transplant (HCT), potentially leading to CMV disease and significant morbidity and mortality. To reduce morbidity and mortality, many centers conduct weekly CMV blood polymerase chain reaction (PCR) surveillance testing with subsequent initiation of antiviral therapy upon CMV DNAemia detection. However, the impact of CMV DNAemia on subsequent hospitalization risk has not been assessed using models accounting for the time-varying nature of the exposure, outcome, and confounders. METHODS: All allogeneic HCTs at the Children's Hospital of Philadelphia from January 2004-April 2017 were considered for inclusion. Patients were monitored with CMV surveillance via PCR testing for up to 105 days after HCT receipt. We estimated the association between CMV DNAemia and rate of hospitalization using marginal structural models (MSM). RESULTS: There were 343 allogeneic HCT episodes in 330 with CMV surveillance; median age was 9.0 (range: 0.1-26.2) and 46.5% were female. And 24.1% of HCT patients had at least one positive CMV blood PCR during the follow-up period. Median time to CMV DNAemia detection was 19 days (range: 4-97). The MSM estimated the incidence rate ratios for an association of CMV DNAemia with hospitalization to be 1.24, (95% confidence interval: 1.04-1.47). CONCLUSIONS: CMV DNAemia was associated with an increased hospitalization in the post-HCT period. The MSM accounted for time-varying nature of the outcome, exposure and confounders. The findings support prevention of CMV DNAemia in this population. We recommend further investigation into the effectiveness and safety of prophylaxis versus pre-emptive CMV prevention approaches.

Emulating a Target Trial Using Primary-Care Electronic Health Records: Sodium-Glucose Cotransporter 2 Inhibitor Medications and Hemoglobin A1c.

Substantial effort has been dedicated to conducting randomized controlled experiments to generate clinical evidence for diabetes treatment. Randomized controlled experiments are the gold standard for establishing cause and effect. However, due to their high cost and time commitment, large observational databases such as those comprised of electronic health record (EHR) data collected in routine primary care may provide an alternative source with which to address such causal objectives. We used a Canadian primary-care data repository housed at the University of Toronto (Toronto, Ontario, Canada) to emulate a randomized experiment. We estimated the effectiveness of sodium-glucose cotransporter 2 inhibitor (SGLT-2i) medications for patients with diabetes using hemoglobin A1c (HbA1c) as a primary outcome and marker for glycemic control from 2018 to 2021. We assumed an intention-to-treat analysis for prescribed treatment, with analyses based on the treatment assigned rather than the treatment eventually received. We defined the causal contrast of interest as the net change in HbA1c (percent) between the group receiving the standard of care versus the group receiving SGLT-2i medication. Using a counterfactual framework, marginal structural models demonstrated a reduction in mean HbA1c level with the initiation of SGLT-2i medications. These findings provided effect sizes similar to those from earlier clinical trials on assessing the effectiveness of SGLT-2i medications.

Marginal structural models for multilevel clustered data.

Marginal structural models (MSMs), which adopt inverse probability treatment weighting in the estimating equations, are powerful tools to estimate the causal effects of time-varying exposures in the presence of time-dependent confounders. Motivated by the Conservation of Hearing Study (CHEARS) Audiology Assessment Arm (AAA) where repeated hearing measurements were clustered by study participants, time, and testing sites, we propose two methods to account for the multilevel correlation structure when fitting the MSMs. The first method directly models the covariance of the repeated outcomes when solving the weighted generalized estimating equations for MSMs, while the second two-stage analysis approach fits cluster-specific MSMs first and then combines the estimated parameters using mixed-effects meta-analysis. Finite sample simulation results suggest that our methods can obtain less biased and more efficient estimates of the parameters by accounting for the multilevel correlation. Moreover, we explore the effects of using fixed- or mixed-effects model to estimate the treatment probability on the parameter estimates of the MSMs in the presence of unmeasured cluster-level confounders. Lastly, we apply our methods to the CHEARS AAA data set, to estimate the causal effects of aspirin use on hearing loss.

Glucocorticoid exposure and the risk of serious infections in rheumatoid arthritis: a marginal structural model application.

OBJECTIVE: Observational studies have reported an increased risk of infections associated with glucocorticoids in RA, not supported by evidence from randomized controlled trials. Inappropriately accommodating time-varying exposure and confounding in observational studies might explain the conflicting results. Therefore, we compared the incidence of serious infections between different oral glucocorticoid dose patterns over three years in a prospective inception cohort, adjusting for time-varying confounders in marginal structural models. METHODS: We included 9654 newly diagnosed RA patients from the Swedish Rheumatology Quality Register between 2007-2018 and followed them for three years after the first rheumatology visit. Follow-up was divided into 90-day periods. A mean oral prednisone daily dose was calculated for each period and categorized into 'no use', 'low' (≤10 mg/day) and 'high' (>10 mg/day) doses. The incidence of serious infections (hospitalization for infection) over follow-up periods was modelled by pooled logistic regression allowing separate effects for recent and past exposure. RESULTS: An increased incidence of serious infections was associated with higher compared with lower doses and with more recent compared with past glucocorticoid exposure. Over 3 years of follow-up, the marginal structural models predicted one additional serious infection for every 83 individuals treated with low GC doses for the first 6 months, and for every 125 individuals treated with high GC doses for the first 3 months, compared with no GC use. CONCLUSION: Our results broadly agree with previous observational studies showing a dose dependent increased risk of infection associated with (recent) use of oral glucocorticoids.

Marginal structural models with monotonicity constraints: A case study in out-of-hospital cardiac arrest patients.

This paper deals with estimating the probability of a binary counterfactual outcome as a function of a continuous covariate under monotonicity constraints. We are motivated by the study of out-of-hospital cardiac arrest patients which aims to estimate the counterfactual 30-day survival probability if either all patients had received, or if none of the patients had received bystander cardiopulmonary resuscitation (CPR), as a function of the ambulance response time. It is natural to assume that the counterfactual 30-day survival probability cannot increase with increasing ambulance response time. We model the monotone relationship with a marginal structural model and B-splines. We then derive an estimating equation for the parameters of interest which however further relies on an auxiliary regression model for the observed 30-day survival probabilities. The predictions of the observed 30-day survival probabilities are used as pseudo-values for the unobserved counterfactual 30-day survival status. The methods are illustrated and contrasted with an unconstrained modeling approach in large-scale Danish registry data.

Causal inference for recurrent events via aggregated marginal odds ratio.

Researchers often work with treatments and outcomes that vary over time. For example, psychologists are interested in the curative effect of cognitive behavior therapies on patients' recurrent depression symptoms. While there are various causal effect measures designed for one-time treatment, the causal effect measures for time-varying treatment and recurrent events are relatively under-developed. In this article, a new causal measure is proposed to quantify the causal effect of time-varying treatments on recurrent events. We suggest estimators with robust standard errors that are based on various weight models for both conventional causal measures and the proposed measure in different time settings. We outline the approaches and describe how using some stabilized inverse probability weight models are more advantageous than others. We demonstrate that the proposed causal estimand can be consistently estimated for study periods of moderate length, and the estimation results are compared under different treatment settings with various weight models. We also find that the proposed method is suitable for both absorbing and nonabsorbing treatments. The methods are applied to the 1997 National Longitudinal Study of Youth as an illustrative example.

Estimation of marginal structural models under irregular visits and unmeasured confounder: calibrated inverse probability weights.

Clinical information collected in electronic health records (EHRs) is becoming an essential source to emulate randomized experiments. Since patients do not interact with the healthcare system at random, the longitudinal information in large observational databases must account for irregular visits. Moreover, we need to also account for subject-specific unmeasured confounders which may act as a common cause for treatment assignment mechanism (e.g. glucose-lowering medications) while also influencing the outcome (e.g. Hemoglobin A1c). We used the calibration of longitudinal weights to improve the finite sample properties and to account for subject-specific unmeasured confounders. A Monte Carlo simulation study is conducted to evaluate the performance of calibrated inverse probability estimators using time-dependent treatment assignment and irregular visits with subject-specific unmeasured confounders. The simulation study showed that the longitudinal weights with calibrated restrictions improved the finite sample bias when compared to the stabilized weights. The application of the calibrated weights is demonstrated using the exposure of glucose lowering medications and the longitudinal outcome of Hemoglobin A1c. Our results support the effectiveness of glucose lowering medications in reducing Hemoglobin A1c among type II diabetes patients with elevated glycemic index ([Formula: see text]) using stabilized and calibrated weights.

Association between coffee and incident heart failure: A prospective cohort study from the UK Biobank.

BACKGROUND AND AIMS: The relationship between coffee consumption and heart failure (HF) incidence is inconclusive. This study aimed to explore the association between time-varying coffee consumption and incident HF using a longitudinal study design. METHODS AND RESULTS: Data were obtained from the UK Biobank, comprising 497,503 adults (age, 56.5 ± 8.1 years; 54.6% women) who were free from HF at baseline in 2006-2010. The median follow-up time for the HF incidence was 11.9 years. Marginal structural models (MSM) were employed to adjust for potential time-varying confounders and account for bias caused by loss of follow-up. Furthermore, we used a restricted cubic spline to test and describe the nonlinear relationship between coffee consumption and HF risk. At baseline, 70.5% of participants reported drinking ≥1 cups/d coffee and 2.7% participants developed HF. After adjusting for potential confounders, we identified a nonlinear J-shaped association between coffee consumption and HF risk (P < 0.001). Compared with drinking coffee <1 cups/d, 1-2 cups/d (HR = 0.878; 95% CI: 0.838-0.920), 3-4 cups/d (HR = 0.920; 95% CI: 0.869-0.974) may be associated with a reduced risk of HF, while >6 cups/d (HR = 1.209; 95% CI: 1.056-1.385) may be associated with a higher risk of HF. However, sensitive analyses stratified by gender and smoking status indicated that >6 cups/d does not significantly increase the risk of HF. Additionally, the type of coffee was found to significant impact on the incidence of HF (P < 0.05). CONCLUSION: In this large cohort of UK adults, moderate coffee consumption may reduce risk of HF incidence.

Marginal Structural Models for Life-Course Theories and Social Epidemiology: Definitions, Sources of Bias, and Simulated Illustrations.

Social epidemiology aims to identify social structural risk factors, thus informing targets and timing of interventions. Ascertaining which interventions will be most effective and when they should be implemented is challenging because social conditions vary across the life course and are subject to time-varying confounding. Marginal structural models (MSMs) may be useful but can present unique challenges when studying social epidemiologic exposures over the life course. We describe selected MSMs corresponding to common theoretical life-course models and identify key issues for consideration related to time-varying confounding and late study enrollment. Using simulated data mimicking a cohort study evaluating the effects of depression in early, mid-, and late life on late-life stroke risk, we examined whether and when specific study characteristics and analytical strategies may induce bias. In the context of time-varying confounding, inverse-probability-weighted estimation of correctly specified MSMs accurately estimated the target causal effects, while conventional regression models showed significant bias. When no measure of early-life depression was available, neither MSMs nor conventional models were unbiased, due to confounding by early-life depression. To inform interventions, researchers need to identify timing of effects and consider whether missing data regarding exposures earlier in life may lead to biased estimates.

Racial Residential Segregation in Young Adulthood and Brain Integrity in Middle Age: Can We Learn From Small Samples?

Racial residential segregation is associated with multiple adverse health outcomes in Black individuals. Yet, the influence of structural racism and racial residential segregation on brain aging is less understood. In this study, we investigated the association between cumulative exposure to racial residential segregation over 25 years (1985-2010) in young adulthood, as measured by the Getis-Ord Gi* statistic, and year 25 measures of brain volume (cerebral, gray matter, white matter, and hippocampal volumes) in midlife. We studied 290 Black participants with available brain imaging data who were enrolled in the Coronary Artery Risk Development in Young Adults (CARDIA) Study, a prospective cohort study. CARDIA investigators originally recruited 2,637 Black participants aged 18-30 years from 4 field centers across the United States. We conducted analyses using marginal structural models, incorporating inverse probability of treatment weighting and inverse probability of censoring weighting. We found that compared with low/medium segregation, greater cumulative exposure to a high level of racial residential segregation throughout young adulthood was associated with smaller brain volumes in general (e.g., for cerebral volume, ß = -0.08, 95% confidence interval: -0.15, -0.02) and with a more pronounced reduction in hippocampal volume, though results were not statistically significant. Our findings suggest that exposure to segregated neighborhoods may be associated with worse brain aging.

Metformin Treatment Among Men With Diabetes and the Risk of Prostate Cancer: A Population-Based Historical Cohort Study.

There is conflicting evidence regarding the association between metformin treatment and prostate cancer risk in diabetic men. We investigated this association in a population-based Israeli cohort of 145,617 men aged 21-89 years with incident diabetes who were followed over the period 2002-2012. We implemented a time-dependent covariate Cox model, using weighted cumulative exposure to relate metformin history to prostate cancer risk, adjusting for use of other glucose-lowering medications, age, ethnicity, and socioeconomic status. To adjust for time-varying glucose control variables, we used inverse probability weighting of a marginal structural model. With 666,553 person-years of follow-up, 1,592 men were diagnosed with prostate cancer. Metformin exposure in the previous year was positively associated with prostate cancer risk (per defined daily dose; without adjustment for glucose control, hazard ratio (HR) = 1.53 (95% confidence interval (CI): 1.19, 1.96); with adjustment, HR = 1.42 (95% CI: 1.04, 1.94)). However, exposure during the previous 2-7 years was negatively associated with risk (without adjustment for glucose control, HR = 0.58 (95% CI: 0.37, 0.93); with adjustment, HR = 0.60 (95% CI: 0.33, 1.09)). These positive and negative associations with previous-year and earlier metformin exposure, respectively, need to be confirmed and better understood.

Statin exposure is associated with reduced development of acute-on-chronic liver failure in a Veterans Affairs cohort.

BACKGROUNDS & AIMS: There is a need to identify therapies that prevent the development of acute-on-chronic liver failure (ACLF) in patients with cirrhosis. This study sought to evaluate the association between statin exposure and the risk of developing ACLF in a large national cohort of patients with cirrhosis. METHODS: We performed a retrospective cohort study of patients diagnosed with cirrhosis within the Veterans Health Administration from 2008 and 2018. Patients were stratified into 3 groups based on statin exposure (statin naïve, existing statin user, and new statin initiator). Cox proportional hazards regression models with inverse probability treatment weighting and marginal structural models were utilized to comprehensively address potential confounding in estimating the association between time-updated statin exposure and first occurrence of high-grade ACLF. RESULTS: The cohort included 84,963 patients, of whom 26.9% were on a statin at baseline. A total of 8,558 (10.1%) patients with cirrhosis were hospitalized with high-grade ACLF over a median follow-up time of 51.6 months (IQR 27.5-81.4). Time-updated statin use was associated with a significant reduction in the hazard of developing ACLF (hazard ratio [HR] 0.62, 95% CI 0.59-0.65, p <0.001). Increasing doses of statin were associated with progressively reduced hazard of developing ACLF (HR 0.75, 95% CI 0.66-0.86, p <0.001 for <20 mg vs. 0 mg of time-updated statin exposure, in simvastatin equivalents; HR 0.61, 95%, CI 0.58-0.64, p <0.001 for >20 mg vs. 0 mg statin exposure). Furthermore, every additional 5 months of statin exposure was associated with a 9% reduced hazard of high-grade ACLF (HR 0.91, 95% CI 0.90-0.92, p <0.001). CONCLUSIONS: In this large, retrospective, cohort study in patients with cirrhosis, statin use was significantly associated with reduced development of high-grade ACLF. LAY SUMMARY: Statin therapy has been shown to have numerous beneficial effects in patients with chronic liver disease. This study demonstrated a strong association between statin therapy and a reduced risk of acute-on-chronic liver failure development in patients with cirrhosis. The results of this study support the promising role that statins may play in future prevention of acute-on-chronic liver failure in patients with cirrhosis.

Causal effects of cingulate morphology on executive functions in healthy young adults.

In this study, we want to explore evidence for the causal relationship between the anatomical descriptors of the cingulate cortex (surface area, mean curvature-corrected thickness, and volume) and the performance of cognitive tasks such as Card Sort, Flanker, List Sort used as instruments to measure the executive functions of flexibility, inhibitory control, and working memory. We have performed this analysis in a cross-sectional sample of 899 healthy young subjects of the Human Connectome Project. To the best of our knowledge, this is the first study using causal inference to explain the relationship between cingulate morphology and the performance of executive tasks in healthy subjects. We have tested the causal model under a counterfactual framework using stabilized inverse probability of treatment weighting and marginal structural models. The results showed that the posterior cingulate surface area has a positive causal effect on inhibition (Flanker task) and cognitive flexibility (Card Sort). A unit increase (+1 mm2 ) in the posterior cingulate surface area will cause a 0.008% and 0.009% increase from the National Institute of Health (NIH) normative mean in Flankers (p-value <0.001), and Card Sort (p-value 0.005), respectively. Furthermore, a unit increase (+1 mm2 ) in the anterior cingulate surface area will cause a 0.004% (p-value <0.001) and 0.005% (p-value 0.001) increase from the NIH normative mean in Flankers and Card Sort. In contrast, the curvature-corrected-mean thickness only showed an association for anterior cingulate with List Sort (p = 0.034) but no causal effect.

Causal inference methods for vaccine sieve analysis with effect modification.

The protective effects of vaccines may vary depending on individual characteristics, such as age. Traditionally, such effect modification has been examined with subgroup analyses or inclusion of cross-product terms in regression frameworks. However, in many vaccine settings, effect modification may also depend on the infecting pathogen's characteristics, which are measured postrandomization. Sieve analysis examines whether such effects are present by combining pathogen genetic sequence information with individual-level data and can generate new hypotheses on the pathways whereby vaccines provide protection. In this article, we develop a causal framework for evaluating effect modification in the context of sieve analysis. Our approach can be used to assess the magnitude of sieve effects and, in particular, whether these effects are modified by individual-level characteristics. Our method accounts for difficulties occurring in real-world data analysis, such as competing risks, nonrandomized treatments, and differential dropout. Our approach also integrates modern machine learning techniques. We demonstrate the validity and efficiency of our approach in simulation studies and apply the methodology to a malaria vaccine study.