Mediation and mechanism

The concepts of mediation and mechanism are contrasted and logical implications holding between theses two concepts are described. The concept of mediation can be formalized using counterfactual definitions of indirect effects; the concept of mechanism can be formalized within the sufficient cause framework. It is shown that both concepts can be illustrated using a single causal diagram. It is also shown that mediation implies mechanism but mechanism need not imply mediation. Discussion is given regarding how the distinction between “statistical causality” and “mechanistic causality” is blurred by recent work in causal inference concerning methods for testing for mediation and mechanism.     

A multiple mediator analysis approach to quantify the effects of the ADH1B and ALDH2 genes on hepatocellular carcinoma risk

Previous work suggested a genetic component affecting the risk of hepatocellular carcinoma (HCC) and mediation analyses have elucidated potential indirect pathways of these genetic effects. Specifically, the effects of alcohol dehydrogenase (ADH1B) and aldehyde dehydrogenase (ALDH2) genes on HCC risk vary based on alcohol consumption habits. However, alcohol consumption may not be the only mediator in the identified pathway: factors related to alcohol consumption may contribute to the same indirect pathway. Thus, we developed a multimediator model to quantify the genetic effects on HCC risk through sequential dichotomous mediators under the counterfactual framework. Our method provided a closed form formula for the mediation effects through different indirect paths, which requires no assumption for the rarity of outcome. In simulation studies of a finite sample, we presented the utility of the method with the variance of the effects estimated using the delta method and bootstrapping. We applied our method to data from participants in Taiwan (580 cases and 3,207 controls) and quantified the mediation effects of single nucleotide polymorphisms (SNPs) in the ADH1B and ALDH2 genes on HCC through alcohol consumption (yes/no) and high alanine transaminase (ALT) levels (greater than or equal to 45 U/L or below 45 U/L). Assuming a dominant risk model, we identified that the SNPs’ effects through alcohol consumption is more significant than through ALT levels on HCC risk. This new method provides insight to the magnitude of various casual mechanisms as a closed form solution and can be readily applied in other genomic studies.     

On identification of agonistic interaction: Hepatitis B and C interaction on hepatocellular carcinoma

Agonistic interaction is one of the most important types of mechanistic interaction, which is difficult to be distinguished from synergistic interaction by empirical data. In this study, we propose four approaches that suffice to identify and estimate the agonistic interaction: (1) to make a strong assumption that synergism does not exist; (2) to exploit information from a third factor by assuming that this factor is a necessary component for the background condition of synergistic interaction but is not involved in other mechanisms; (3) to consider a third factor necessary for the background condition of agonistic interaction but not involved in other mechanisms; and (4) similar to (3) but to allow flexibility that the third factor may have a main effect on the outcome and/or a synergistic effect with the two risk factors of interest. We applied the proposed methods to quantify the agonism of Hepatitis B and C viruses (HBV and HCV) infections on liver cancer using a Taiwanese cohort study (n = 23 820; HBV carrier n = 4149 (17.44%), HCV carrier n = 1313 (5.52%)). The result demonstrated that agonistic interaction is more dominant compared with synergistic interaction, which explains the findings that the dual infected patients do not have a significantly higher risk of liver cancer than those with single infection. By exploiting an additional risk factor that satisfies certain assumptions, these approaches potentially fill the gap between mechanistic and causal interactions, contributing the comprehensive understanding of causal mechanisms.     

Estimation of causal mediation effects for a dichotomous outcome in multiple‐mediator models using the mediation formula

Mediators are intermediate variables in the causal pathway between an exposure and an outcome. Mediation analysis investigates the extent to which exposure effects occur through these variables, thus revealing causal mechanisms. In this paper, we consider the estimation of the mediation effect when the outcome is binary and multiple mediators of different types exist. We give a precise definition of the total mediation effect as well as decomposed mediation effects through individual or sets of mediators using the potential outcomes framework. We formulate a model of joint distribution (probit‐normal) using continuous latent variables for any binary mediators to account for correlations among multiple mediators. A mediation formula approach is proposed to estimate the total mediation effect and decomposed mediation effects based on this parametric model. Estimation of mediation effects through individual or subsets of mediators requires an assumption involving the joint distribution of multiple counterfactuals. We conduct a simulation study that demonstrates low bias of mediation effect estimators for two‐mediator models with various combinations of mediator types. The results also show that the power to detect a nonzero total mediation effect increases as the correlation coefficient between two mediators increases, whereas power for individual mediation effects reaches a maximum when the mediators are uncorrelated. We illustrate our approach by applying it to a retrospective cohort study of dental caries in adolescents with low and high socioeconomic status. Sensitivity analysis is performed to assess the robustness of conclusions regarding mediation effects when the assumption of no unmeasured mediator‐outcome confounders is violated. Copyright © 2013 John Wiley & Sons, Ltd.     

Mediation analysis via potential outcomes models

This paper develops a causal or manipulation model framework for mediation analysis based on the concept of potential outcome. Using this framework, we provide new definitions and measures of mediation. Effects of manipulations are modeled via the linear structural model. Corresponding structural equation models (SEMs), in conjunction with two-stage least-squares estimation and the delta method, are used to perform inference. The methods are applied to data from a study of nursing interventions for postoperative pain. We address the cases of more than two treatment groups, and an interaction among mediators. For the latter, a sensitivity analysis approach to handle unidentified parameters is described. Interpretative advantages of the potential outcomes framework for mediation are emphasized.     

The mediating role of the venules between smoking and ischemic stroke

A potential mechanism by which smoking affects ischemic stroke is through wider venules, but this mediating role of wider venules has never been quantified. Here, we aimed to estimate to what extent the effect of smoking on ischemic stroke is possibly mediated by the venules via the recently developed four-way effect decomposition. This study was part of a population-based study including 9109 stroke-free persons participated in the study in 1990, 2004, or 2006 (mean age: 63.7 years; 58% women). Smoking behavior (smoking versus non-smoking) was identified by interview. Retinal venular calibers were measured semi-automatically on retinal photographs. Incident strokes were assessed until January 2016. A regression-based approach was used with venular calibers as mediator to decompose the total effect of smoking compared to non-smoking into four components: controlled direct effect (neither mediation nor interaction), pure indirect effect (mediation only), reference interaction effect (interaction only) and mediated interaction effect (both mediation and interaction). During a mean follow-up of 12.5 years, 665 persons suffered an ischemic stroke. Smoking increased the risk of developing ischemic stroke compared to non-smoking with an excess risk of 0.41 (95% confidence interval 0.10; 0.67). With retinal venules as a potential mediator, the excess relative risk could be decomposed into 77% controlled direct effect, 4% mediation only, 4% interaction only, and 15% mediated interaction. To conclude, in the pathophysiology of ischemic stroke, the effect of smoking on ischemic stroke may partly explained by changes in the venules, where there is both pure mediation and mediated interaction.     

基于四向分解法探索中高强度休闲体力活动与肥胖指标对糖尿病风险的影响

目的 探讨中高强度休闲体力活动（moderate-vigorous recreational physical activity,MVRPA）在肥胖指标和糖尿病之间的中介和交互作用,为糖尿病的预防提供科学指导。方法 选取2020年6—10月参与中山市火炬开发区人民医院及开发区社区卫生服务中心健康体检的4 957名41岁及以上人群作为研究对象,通过四向分解法来评估MVRPA在肥胖指标和糖尿病之间的中介作用和交互作用。结果 在本研究人群中,约有847人（17.09%）患有糖尿病。单因素分析结果显示,MVRPA与身体质量指数（body mass index, BMI）、内脏脂肪指数（Visceral Fat Index,VFI）显著相关,BMI、体脂率（body fat rate,BFR）、VFI和糖尿病之间存在相关性（P＜0.05）。此外,与MVRPA充足的人群相比,MVRPA不足增加了发生糖尿病的风险（OR=1.23,95%CI:1.02~1.48）。基于四向分解法的中介分析表明,MVRPA作为中介变量时,BMI对糖尿病的超额相对风险可分解为78%的参考交互作用（reference interaction,INTref）、21%的控制直接效应（controlled direct effect,CDE）和1%的纯间接效应（pure indirect effect,PIE）,其他肥胖指标（BFR和VFI）与上述结果相似。肥胖指标对糖尿病的超额相对风险可归因于INTref（P＜0.05）。结论 肥胖指标对糖尿病发病的影响可能部分由MVRPA来解释,我们应鼓励肥胖人群保持充足的MVRPA,以期降低糖尿病发生风险。     

Hepatitis C virus genotypes in southern Taiwan: prevalence and clinical implications

The role of hepatitis C virus (HCV) genotypes in the development of hepatocellular carcinoma (HCC) is still controversial. To determine the distribution and clinical implications of HCV genotypes in southern Taiwan, we analysed 418 patients with chronic HCV infections. HCV genotypes were determined using an HCV Line Probe Assay. The predominant HCV genotype was 1b (45.5%), followed by 2a/2c (30.9%) and 2b (6.9%). The prevalence of genotype 1b in HCC patients (60.3%) was significantly higher than in those with liver cirrhosis (38.7%) and chronic hepatitis (38.7%) (P=0.003 and P<0.001, respectively). Patients with chronic HCV 2a/2c infection had higher alanine aminotransferase (ALT) levels than those with chronic HCV 1b infection (P<0.001). Univariate analysis revealed that disease severity was significantly correlated with older age, genotype 1b, lower ALT levels and lower viral load. Based on multiple logistic regression analysis, after adjusting for age and serum HCV RNA levels, HCV 1b infection was still a significant risk factor for HCC. In conclusion, the predominant genotypes in southern Taiwan were 1b and 2a/2c, and disease severity was associated with genotype 1b.     

The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology

The resurgence of interest in the effect of neighborhood contexts on health outcomes, motivated by advances in social epidemiology, multilevel theories and sophisticated statistical models, too often fails to confront the enormous methodological problems associated with causal inference. This paper employs the counterfactual causal framework to illuminate fundamental obstacles in the identification, explanation, and usefulness of multilevel neighborhood effect studies. We show that identifying useful independent neighborhood effect parameters, as currently conceptualized with observational data, to be impossible. Along with the development of a dependency-based methodology and theories of social interaction, randomized community trials are advocated as a superior research strategy, one that may help social epidemiology answer the causal questions necessary for remediating disparities and otherwise improving the public's health.     

Causal interactions in the proportional hazards model

The paper relates estimation and testing for additive interaction in proportional hazards models to causal interactions within the counterfactual framework. A definition of a causal interaction for time-to-event outcomes is given that generalizes existing definitions for dichotomous outcomes. Conditions are given concerning the relative excess risk due to interaction in proportional hazards models that imply the presence of a causal interaction at some point in time. Further results are given that allow for assessing the range of times and baseline survival probabilities for which parameter estimates indicate that a causal interaction is present, and for deriving lower bounds on the prevalence of such causal interactions. An interesting feature of the time-to-event setting is that causal interactions can disappear as time progresses, ie, whether a causal interaction is present depends on the follow-up time. The results are illustrated by hypothetical and data analysis examples.     

Integrative modeling of multi‐platform genomic data under the framework of mediation analysis

Given the availability of genomic data, there have been emerging interests in integrating multi‐platform data. Here, we propose to model genetics (single nucleotide polymorphism (SNP)), epigenetics (DNA methylation), and gene expression data as a biological process to delineate phenotypic traits under the framework of causal mediation modeling. We propose a regression model for the joint effect of SNPs, methylation, gene expression, and their nonlinear interactions on the outcome and develop a variance component score test for any arbitrary set of regression coefficients. The test statistic under the null follows a mixture of chi‐square distributions, which can be approximated using a characteristic function inversion method or a perturbation procedure. We construct tests for candidate models determined by different combinations of SNPs, DNA methylation, gene expression, and interactions and further propose an omnibus test to accommodate different models. We then study three path‐specific effects: the direct effect of SNPs on the outcome, the effect mediated through expression, and the effect through methylation. We characterize correspondences between the three path‐specific effects and coefficients in the regression model, which are influenced by causal relations among SNPs, DNA methylation, and gene expression. We illustrate the utility of our method in two genomic studies and numerical simulation studies. Copyright © 2014 John Wiley & Sons, Ltd.     

Mediation analysis with causally ordered mediators using Cox proportional hazards model

Causal mediation analysis aims to investigate the mechanism linking an exposure and an outcome. However, studies regarding mediation effects on survival outcomes are limited, particularly in multi-mediator settings. The existing multi-mediator analyses for survival outcomes are either performed under special model specifications such as probit models or additive hazard models, or they assume a rare outcome. Here, we propose a novel multi-mediation analysis based on the widely used Cox proportional hazards model without the rare outcome assumption. We develop a methodology under a counterfactual framework to identify path-specific effects (PSEs) of the exposure on the outcome through the mediator(s) and derive the closed-form formula for PSEs on a transformed survival time. Moreover, we show that the convolution of an extreme value and Gaussian random variables converges to another Gaussian, provided that the variance of the original Gaussian gets large. Based on that, we further derive closed-form expressions for PSEs on survival probabilities. Asymptotic properties are established for both estimators. Extensive simulation is conducted to evaluate the finite sample performance of our proposed estimators and to compare with existing methods. The utility of the proposed method is illustrated in a hepatitis study of liver cancer risk.     

A causal framework for classical statistical estimands in failure-time settings with competing events

In failure-time settings, a competing event is any event that makes it impossible for the event of interest to occur. For example, cardiovascular disease death is a competing event for prostate cancer death because an individual cannot die of prostate cancer once he has died of cardiovascular disease. Various statistical estimands have been defined as possible targets of inference in the classical competing risks literature. Many reviews have described these statistical estimands and their estimating procedures with recommendations about their use. However, this previous work has not used a formal framework for characterizing causal effects and their identifying conditions, which makes it difficult to interpret effect estimates and assess recommendations regarding analytic choices. Here we use a counterfactual framework to explicitly define each of these classical estimands. We clarify that, depending on whether competing events are defined as censoring events, contrasts of risks can define a total effect of the treatment on the event of interest or a direct effect of the treatment on the event of interest not mediated by the competing event. In contrast, regardless of whether competing events are defined as censoring events, counterfactual hazard contrasts cannot generally be interpreted as causal effects. We illustrate how identifying assumptions for all of these counterfactual estimands can be represented in causal diagrams, in which competing events are depicted as time-varying covariates. We present an application of these ideas to data from a randomized trial designed to estimate the effect of estrogen therapy on prostate cancer mortality.     

Continuous-time causal mediation analysis

While causal mediation analysis has seen considerable recent development for a single measured mediator (M) and final outcome (Y), less attention has been given to repeatedly measured M and Y. Previous methods have typically involved discrete-time models that limit inference to the particular measurement times used and do not recognize the continuous nature of the mediation process over time. To overcome such limitations, we present a new continuous-time approach to causal mediation analysis that uses a differential equations model in a potential outcomes framework to describe the causal relationships among model variables over time. A connection between the differential equation models and standard repeated measures models is made to provide convenient model formulation and fitting. A continuous-time extension of the sequential ignorability assumption allows for identifiable natural direct and indirect effects as functions of time, with estimation based on a two-step approach to model fitting in conjunction with a continuous-time mediation formula. Novel features include a measure of an overall mediation effect based on the “area between the curves,” and an approach for predicting the effects of new interventions. Simulation studies show good properties of estimators and the new methodology is applied to data from a cohort study to investigate sugary drink consumption as a mediator of the effect of socioeconomic status on dental caries in children.     

A mapping between interactions and interference: implications for vaccine trials

In this paper, we discuss relationships between causal interactions within the counterfactual framework and interference in which the exposure of one person may affect the outcomes of another. We show that the empirical tests for causal interactions can, in fact, all be adapted to empirical tests for particular forms of interference. In the context of interference, by recoding the response as some function of the outcomes of the various persons within a cluster, a wide range of different forms of interference can potentially be detected. The correspondence between causal interactions and forms of interference extends to encompass n-way causal interactions, interference between n persons within a cluster, and multivalued exposures. The theory for causal interactions provides a complete conceptual apparatus for assessing interference as well. The results are illustrated using data from a hypothetical vaccine trial to reason about specific forms of interference and spillover effects that may be present in this vaccine setting. We discuss the implications of this correspondence for our conceptualizations of interaction and for application to vaccine trials and many other settings in which spillover effects may be present.     

A sufficient cause based approach to the assessment of mediation

The minimal sufficient cause (MSC) model, also known as the sufficient component cause model, has been used to facilitate understanding of several key concepts in epidemiology. To improve the understanding of mediation, we introduce a causal model for mediation that is grounded in the MSC approach. First, we describe an unbiased model for mediation, to clarify the causal meaning of previously described indirect effects. Through the use of potential outcomes and response types, we express each indirect (and direct) effect in terms of component causes within the MSC model. Second, we use an MSC-based model to illustrate a common cause of the mediator and outcome, i.e. a confounder of the mediator–outcome relationship. By describing this potential source of bias within the MSC-based model, important complexities are noted that impact the magnitude of plausible confounding. In conclusion, an MSC-based approach leads to several important insights concerning the interpretation of indirect and direct effects, as well as the potential sources of bias in mediation analysis.     

Estimation of direct causal effects

Many common problems in epidemiologic and clinical research involve estimating the effect of an exposure on an outcome while blocking the exposure's effect on an intermediate variable. Effects of this kind are termed direct effects. Estimation of direct effects is typically the goal of research aimed at understanding mechanistic pathways by which an exposure acts to cause or prevent disease, as well as in many other settings. Although multivariable regression is commonly used to estimate direct effects, this approach requires assumptions beyond those required for the estimation of total causal effects. In addition, when the exposure and intermediate variables interact to cause disease, multivariable regression estimates a particular type of direct effect-the effect of an exposure on an outcome when the intermediate is fixed at a specified level. Using the counterfactual framework, we distinguish this definition of a direct effect (controlled direct effect) from an alternative definition, in which the effect of the exposure on the intermediate is blocked, but the intermediate is otherwise allowed to vary as it would in the absence of exposure (natural direct effect). We illustrate the difference between controlled and natural direct effects using several examples. We present an estimation approach for natural direct effects that can be implemented using standard statistical software, and we review the assumptions underlying our approach (which are less restrictive than those proposed by previous authors).     

Estimation of mediation effects for zero‐inflated regression models

The goal of mediation analysis is to identify and explicate the mechanism that underlies a relationship between a risk factor and an outcome via an intermediate variable (mediator). In this paper, we consider the estimation of mediation effects in zero‐inflated (ZI) models intended to accommodate ‘extra’ zeros in count data. Focusing on the ZI negative binomial models, we provide a mediation formula approach to estimate the (overall) mediation effect in the standard two‐stage mediation framework under a key sequential ignorability assumption. We also consider a novel decomposition of the overall mediation effect for the ZI context using a three‐stage mediation model. Estimation of the components of the overall mediation effect requires an assumption involving the joint distribution of two counterfactuals. Simulation study results demonstrate low bias of mediation effect estimators and close‐to‐nominal coverage probability of confidence intervals. We also modify the mediation formula method by replacing ‘exact’ integration with a Monte Carlo integration method. The method is applied to a cohort study of dental caries in very low birth weight adolescents. For overall mediation effect estimation, sensitivity analysis was conducted to quantify the degree to which key assumption must be violated to reverse the original conclusion. Copyright © 2012 John Wiley & Sons, Ltd.     

Steady state trials: another valid substitution of counterfactual ideal to measure causal effects

Objectives

Many traditionally established medical interventions are not examined with randomized trials especially in emergency medicine. We researched what is the scientific basis of the measurement of the causal effect in these interventions and proposed another trial to measure causal effects.

Methods

We deduced steady state trials from the counterfactual model and used Bayesian approaches to estimate causal effects statistically.

Results

When the state of the observed person is fairly steady before an exposure, the ratio of the after-period to the before-period of the exposure is sufficiently small, and changes are obtained in relatively short time, it is possible to postulate that the state of the counterfactual person to be compared is almost equal to the state of the real person before the exposure. Bayesian approaches show that the causal effect of the exposure is estimated even in only one-person steady state trials, when large changes are observed.

Conclusions

Steady state trials are valid methods to measure causal effects and can measure causal effects even in one-person trials. When we can measure the causal effect of interventions with steady state trials, these interventions should be regarded as scientific without use of randomized trials.