A formal causal interpretation of the case-crossover design.

The case-crossover design of Maclure is widely used in epidemiology and other fields to study causal effects of transient treatments on acute outcomes. However, its validity and causal interpretation have only been justified under informal conditions. Here, we place the design in a formal counterfactual framework for the first time. Doing so helps to clarify its assumptions and interpretation. In particular, when the treatment effect is nonnull, we identify a previously unnoticed bias arising from strong common causes of the outcome at different person-times. We analyze this bias and demonstrate its potential importance with simulations. We also use our derivation of the limit of the case-crossover estimator to analyze its sensitivity to treatment effect heterogeneity, a violation of one of the informal criteria for validity. The upshot of this work for practitioners is that, while the case-crossover design can be useful for testing the causal null hypothesis in the presence of baseline confounders, extra caution is warranted when using the case-crossover design for point estimation of causal effects.

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.

Testing indirect effect with a complete or incomplete dichotomous mediator.

Past methodological research on mediation analysis mainly focused on situations where all variables were complete and continuous. When issues of categorical data occur combined with missing data, more methodological considerations are involved. Specifically, appropriate decisions need to be made on estimation methods of the indirect effects and on confidence intervals for testing the indirect effects with accommodations of missing data. We compare strategies that address these issues based on a model with a dichotomous mediator, aiming to provide guidelines for researchers facing such challenges in practice.

A region-based method for causal mediation analysis of DNA methylation data.

Exposure to environmental factors can affect DNA methylation at a 5'-cytosine-phosphate-guanine-3' (CpG) site or a genomic region, which can then affect an outcome. In other words, environmental effects on an outcome could be mediated by DNA methylation. To date, single CpG-site-based mediation analysis has been employed extensively. More recently, however, there has been considerable interest in studying differentially methylated regions (DMRs), both because DMRs are more likely to have functional effects than single CpG sites and because testing DMRs reduces multiple testing. In this report, we propose a novel causal mediation approach under the counterfactual framework to test the significance of total (TE), direct (DE), and indirect effects (IE) of predictors on response variable with a methylated region (MR) as the mediator (denoted as MR-Mediation). Functional linear transformation is used to reduce the possible high dimension of the CpG sites in a predefined MR and to account for their location information. In our simulation studies, MR-Mediation retained the desired Type I error rates for TE, DE, and IE tests. Furthermore, MR-Mediation had better power performance than testing mean methylation level as the mediator in most considered scenarios, especially for IE (i.e., mediated effect) test, which could be more interesting than the other two effect tests. We further illustrate our proposed method by analysing the methylation mediated effect of exposure to gun violence on total immunoglobulin E or atopic asthma among participants in the Epigenetic Variation and Childhood Asthma in Puerto Ricans study.

Disentangling the relationships between lameness, milking frequency and milk production in Dutch dairy herds using an automatic milking system.

Lameness in dairy cattle commonly results in milk production losses. Previous studies have quantified this effect in cows milked with conventional milking systems. The effect of lameness on milk production in herds with automatic milking systems (AMS) might differ because lame cows may visit the AMS less frequently, resulting in an additional milk production loss. This observational study aimed to quantify the direct and indirect effect, through milking frequency, of lameness on milk production in AMS herds by means of a multilevel mediation analysis. Lameness scores, the number of visits to the AMS, and daily milk production records of 1608 dairy cows from 87 Dutch AMS herds were available for analysis. Of those cows, 27 % were mildly lame and 11 % were severely lame. Using a multilevel Poisson regression model, both mildly lame and severely lame cows were identified to visit the AMS less frequently. A separate multilevel linear regression model identified that the direct effect of severe lameness on milk production was 1.4 kg per day. The subsequent multilevel mediation analysis showed that the relationship between severe lameness and milk production was mediated through a reduced milking frequency; 63 % of the total milk production losses (3.9 kg per day) were due to a reduced milking frequency (2.5 kg per day). The mediating effect of a reduced milking frequency was most prominent in cows with parity ≥ 4 and in cows that were ≥ 30 days in lactation. This study suggests that milk production losses due to severe lameness of dairy cows might be higher in AMS herds compared with cows in herds with a conventional milking system, a likely result of a reduced milking frequency of those cows. Prevention of severe lameness may be more cost-effective in AMS herds than in herds with a conventional milking system.

Impact of Social Participation Types on Depression in the Elderly in China: An Analysis Based on Counterfactual Causal Inference.

Objectives: Depression is the leading cause of ill health and disability worldwide, and has become one of the key issues in the public health sector. Social participation is one of the most important measures to support the rapidly aging population and can reduce the risk of depression in the elderly. This study aims to explore the causal relationship between different types of social participation and depression in the elderly, and reduce the level of depression in the elderly by promoting social participation. Methods: In order to accurately evaluate the causal relationship between participation in different types of social activities and depression in the elderly, this paper uses propensity score matching (PSM) for analysis based on counterfactual framework. The specific matching methods used are: k-nearest neighbor matching method, kernel matching method and radius matching method. Results: In this study, 31.4% of the elderly have depression, and the proportion of female elderly is significantly higher. Participation in different social activities has different effects on depression in the elderly. Taking into account sample selection bias, participation in friend-making, exercise, and recreational activities can effectively reduce the risk of depression in the elderly. Compared with other social activities, participation in exercise and recreational activities are more helpful to reduce the risk of depression in the elderly. Participating in exercise activities only reduces the risk of depression in 60-69-year-olds, while participating in economic activities increases the risk of depression in the elderly aged 70 and over. Compared with the male elderly, participation in friend-making, exercise, and recreational activities results in the female elderly having stronger resistance to the risk of depression. Conclusions: Depression was prevalent among the elderly. Through PSM analysis, different types of social participation have different effects on depression in the elderly. In order to maximize the positive effects of different types of social participation on depression in the elderly, it is necessary to provide differentiated social support environment for the elderly. Expanding the research on the relationship between social participation and depression of the elderly will help to formulate more reasonable public health policies and improve the mental health of the elderly.

Plant-Based Diets and the Incidence of Asthma Symptoms among Elderly Women, and the Mediating Role of Body Mass Index.

We aimed to test the hypothesis that adherence to a healthful plant-based diet (hPDI) is associated with a subsequent decrease in the incidence of asthma symptoms, with an opposite association with adherence to an unhealthful plant-based diet (uPDI). In addition, we evaluated a potential mediating role of body mass index (BMI) and the modifying effect of smoking. Among 5700 elderly women from the French Asthma-E3N study with dietary data in 1993 and 2005, we assessed the incidence of asthma symptoms in 2018 among women with no asthma symptoms in 2011. BMI was evaluated in 2008. Mediation analyses in the counterfactual framework were used to disentangle total, direct, and indirect effects mediated by BMI. We found that both healthful and unhealthful plant-based diets were associated with a lower incidence of asthma symptoms over time, mediated by BMI (OR (95%CI) for the indirect effect: 0.94 (0.89-1.00) for hPDI and 0.92 (0.70-1.00) for uPDI)). Associations with both healthful and unhealthful PDIs were mediated by changes in BMI by 33% and 89%, respectively. Plant-based diets (healthful and unhealthful) were associated with subsequently reduced incidences of asthma symptoms over time, partly or almost totally mediated by BMI according to their nutritional quality.

Counterfactual clinical prediction models could help to infer individualized treatment effects in randomized controlled trials-An illustration with the International Stroke Trial.

OBJECTIVE: Causal treatment effects are estimated at the population level in randomized controlled trials, while clinical decision is often to be made at the individual level in practice. We aim to show how clinical prediction models used under a counterfactual framework may help to infer individualized treatment effects. STUDY DESIGN AND SETTING: As an illustrative example, we reanalyze the International Stroke Trial. This large, multicenter trial enrolled 19,435 adult patients with suspected acute ischemic stroke from 36 countries, and reported a modest average benefit of aspirin (vs. no aspirin) on a composite outcome of death or dependency at 6 months. We derive and validate multivariable logistic regression models that predict the patient counterfactual risks of outcome with and without aspirin, conditionally on 23 predictors. RESULTS: The counterfactual prediction models display good performance in terms of calibration and discrimination (validation c-statistics: 0.798 and 0.794). Comparing the counterfactual predicted risks on an absolute difference scale, we show that aspirin-despite an average benefit-may increase the risk of death or dependency at 6 months (compared with the control) in a quarter of stroke patients. CONCLUSIONS: Counterfactual prediction models could help researchers and clinicians (i) infer individualized treatment effects and (ii) better target patients who may benefit from treatments.

Incorporating Bayesian methods into the propensity score matching framework: A no-treatment effect safety analysis.

The propensity score matching method has been used to estimate safety countermeasure (treatment) effects from observational crash data. Within the counterfactual framework, propensity score matching is used to balance the covariates between treatment and control groups. Recent studies in traffic safety research have demonstrated the strength of this method in reducing the bias caused by treatment site selection. However, several general issues associated with safety effect estimates may still influence the effectiveness and robustness of this method. In the present study, Bayesian methods were integrated into the propensity score matching method. Bayesian models are known for their ability to capture heterogeneity and modeling uncertainty. This may help mitigate unobserved variable effects in the roadway and crash data. Furthermore, the sampling-based algorithm used for Bayesian estimation yields more consistent estimates in small region analysis than estimates from frequentist modeling. In this study, a dataset that was used to evaluate the safety effects of the dual application of shoulder and centerline rumble strips on two-lane rural highways was acquired. Only data from the before treatment period were used in a no-treatment effect analysis in order to compare the results of a Bayesian propensity score analysis to a frequentist propensity score analysis. Because no treatment was applied during the analysis period, it was assumed that there would be no treatment effect, or a crash modification factor equal to 1.0. The Bayesian propensity score matching method nominally outperformed the frequentist propensity score matching method in the largest sample and produced near-identical results in the medium sample, but neither method closely approximated the assumed, true crash modification factor in the small sample analysis. A simulation study is recommended to further study the effects of sample size and confounding factors when comparing the Bayesian and frequentist propensity score matching methods.

[A new mediation analysis method for multiple mediators].

Mediation analysis is mainly used to explore the causal mechanism between independent variable X and dependent variable Y. It determines whether mediator M plays a role and evaluate the role's degree in the causal path by decomposing the causal path between the independent variable X and the dependent variable Y. However, the classical mediation analysis is generally used for single mediator. This paper introduces a new mediation analysis method for multiple mediators.