一种新的针对多个中介变量的中介分析方法

中介分析主要用于探究自变量X与因变量Y之间的因果关系机制,将自变量X与因变量Y之间的因果路径进行分解,判断中介变量M是否在其因果路径中起作用及其作用大小。经典的中介分析方法一般仅针对单一中介变量。本文介绍了一种新的针对多个中介变量的中介分析方法。     

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.     

Inverse odds ratio‐weighted estimation for causal mediation analysis

An important scientific goal of studies in the health and social sciences is increasingly to determine to what extent the total effect of a point exposure is mediated by an intermediate variable on the causal pathway between the exposure and the outcome. A causal framework has recently been proposed for mediation analysis, which gives rise to new definitions, formal identification results and novel estimators of direct and indirect effects. In the present paper, the author describes a new inverse odds ratio‐weighted approach to estimate so‐called natural direct and indirect effects. The approach, which uses as a weight the inverse of an estimate of the odds ratio function relating the exposure and the mediator, is universal in that it can be used to decompose total effects in a number of regression models commonly used in practice. Specifically, the approach may be used for effect decomposition in generalized linear models with a nonlinear link function, and in a number of other commonly used models such as the Cox proportional hazards regression for a survival outcome. The approach is simple and can be implemented in standard software provided a weight can be specified for each observation. An additional advantage of the method is that it easily incorporates multiple mediators of a categorical, discrete or continuous nature. Copyright © 2013 John Wiley & Sons, Ltd.     

Mediation analysis for common binary outcomes

Mediation analysis provides an attractive causal inference framework to decompose the total effect of an exposure on an outcome into natural direct effects and natural indirect effects acting through a mediator. For binary outcomes, mediation analysis methods have been developed using logistic regression when the binary outcome is rare. These methods will not hold in practice when a disease is common. In this paper, we develop mediation analysis methods that relax the rare disease assumption when using logistic regression. We calculate the natural direct and indirect effects for common diseases by exploiting the relationship between logit and probit models. Specifically, we derive closed-form expressions for the natural direct and indirect effects on the odds ratio scale. Mediation models for both continuous and binary mediators are considered. We demonstrate through simulation that the proposed method performs well for common binary outcomes. We apply the proposed methods to analyze the Normative Aging Study to identify DNA methylation sites that are mediators of smoking behavior on the outcome of obstructed airway function.     

Global test for high-dimensional mediation: Testing groups of potential mediators

We address the problem of testing whether a possibly high-dimensional vector may act as a mediator between some exposure variable and the outcome of interest. We propose a global test for mediation, which combines a global test with the intersection-union principle. We discuss theoretical properties of our approach and conduct simulation studies that demonstrate that it performs equally well or better than its competitor. We also propose a multiple testing procedure, ScreenMin, that provides asymptotic control of either familywise error rate or false discovery rate when multiple groups of potential mediators are tested simultaneously. We apply our approach to data from a large Norwegian cohort study, where we look at the hypothesis that smoking increases the risk of lung cancer by modifying the level of DNA methylation.     

Testing for the indirect effect under the null for genome‐wide mediation analyses

Mediation analysis helps researchers assess whether part or all of an exposure's effect on an outcome is due to an intermediate variable. The indirect effect can help in designing interventions on the mediator as opposed to the exposure and better understanding the outcome's mechanisms. Mediation analysis has seen increased use in genome‐wide epidemiological studies to test for an exposure of interest being mediated through a genomic measure such as gene expression or DNA methylation (DNAm). Testing for the indirect effect is challenged by the fact that the null hypothesis is composite. We examined the performance of commonly used mediation testing methods for the indirect effect in genome‐wide mediation studies. When there is no association between the exposure and the mediator and no association between the mediator and the outcome, we show that these common tests are overly conservative. This is a case that will arise frequently in genome‐wide mediation studies. Caution is hence needed when applying the commonly used mediation tests in genome‐wide mediation studies. We evaluated the performance of these methods using simulation studies, and performed an epigenome‐wide mediation association study in the Normative Aging Study, analyzing DNAm as a mediator of the effect of pack‐years on FEV₁.     

Mediation analysis of multiple mediators with incomplete omics data

There is an increasing interest in using multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation, protein expressions, and metabolic profiles) to study how the relationships between phenotypes and genotypes may be mediated by other omics markers. Genotypes and phenotypes are typically available for all subjects in genetic studies, but typically, some omics data will be missing for some subjects, due to limitations such as cost and sample quality. In this article, we propose a powerful approach for mediation analysis that accommodates missing data among multiple mediators and allows for various interaction effects. We formulate the relationships among genetic variants, other omics measurements, and phenotypes through linear regression models. We derive the joint likelihood for models with two mediators, accounting for arbitrary patterns of missing values. Utilizing computationally efficient and stable algorithms, we conduct maximum likelihood estimation. Our methods produce unbiased and statistically efficient estimators. We demonstrate the usefulness of our methods through simulation studies and an application to the Metabolic Syndrome in Men study.     

Perceived Stress Mediates the Association between Deployment Sexual Trauma and Nicotine Dependence in Women Veterans

BackgroundRates of smoking and related health consequences are higher for women veterans as compared with their civilian counterparts, and trauma is a known risk factor associated with smoking. Military sexual trauma is prevalent among women veterans and associated with deleterious health outcomes, including tobacco use. However, research has not examined variables that may explain this association. The purpose of the present study was to examine the association between deployment sexual trauma (DST; military sexual trauma that occurs during deployment) and nicotine dependence, and whether perceived stress is a potential explanatory variable (i.e., mediator) in this relationship.MethodsCross-sectional associations and Hayes mediation models were examined using baseline interview data from the Survey of Experiences of Returning Veterans sample (352 recently returned women veterans).ResultsDST was associated with postdeployment nicotine dependence and greater perceived stress. Further, perceived stress was a significant mediator between DST and binary nicotine dependence (indirect effect [standard error] of DST on nicotine dependence through perceived stress, 0.04 [0.01]; 95% confidence interval, 0.01–0.07; odds ratio, 1.04; p < .01) when controlling for education.ConclusionsFindings suggest that perceived stress may be a clinical target for decreasing nicotine dependence among women veterans who have experienced DST.     

Simplifying a prognostic model: a simulation study based on clinical data

Prognostic models are designed to predict a clinical outcome in individuals or groups of individuals with a particular disease or condition. To avoid bias many researchers advocate the use of full models developed by prespecifying predictors. Variable selection is not employed and the resulting models may be large and complicated. In practice more parsimonious models that retain most of the prognostic information may be preferred. We investigate the effect on various performance measures, including mean square error and prognostic classification, of three methods for estimating full models (including penalized estimation and Tibshirani's lasso) and consider two methods (backwards elimination and a new proposal called stepdown) for simplifying full models. Simulation studies based on two medical data sets suggest that simplified models can be found that perform nearly as well as, or sometimes even better than, full models. Optimizing the Akaike information criterion appears to be appropriate for choosing the degree of simplification.     

Multiple mediation analysis with survival outcomes: With an application to explore racial disparity in breast cancer survival

Mediation analysis allows the examination of effects of a third variable in the pathway between an exposure and an outcome. The general multiple mediation analysis method, proposed by Yu et al, improves traditional methods (eg, estimation of natural and controlled direct effects) to enable consideration of multiple mediators/confounders simultaneously and the use of linear and nonlinear predictive models for estimating mediation/confounding effects. In this paper, we extend the method for time-to-event outcomes and apply the method to explore the racial disparity in breast cancer survivals. Breast cancer is the most common cancer and the second leading cause of cancer death among women of all races. Despite improvement of survival rates of breast cancer in the US, a significant difference between white and black women remains. Previous studies have found that more advanced and aggressive tumors and less than optimal treatment may explain the lower survival rates for black women as compared to white women. Due to limitations of current analytic methods and the lack of comprehensive data sets, researchers have not been able to differentiate the relative effect each factor contributes to the overall racial disparity. We use the CDC-funded Patterns of Care study to examine the determinants of racial disparities in breast cancer survival using a novel multiple mediation analysis. Using the proposed method, we applied the Cox hazard model and multiple additive regression trees as predictive models and found that all racial disparity in survival among Louisiana breast cancer patients were explained by factors included in the study.     

Performance and Robustness of Penalized and Unpenalized Methods for Genetic Prediction of Complex Human Disease

A central goal of medical genetics is to accurately predict complex disease from genotypes. Here, we present a comprehensive analysis of simulated and real data using lasso and elastic‐net penalized support‐vector machine models, a mixed‐effects linear model, a polygenic score, and unpenalized logistic regression. In simulation, the sparse penalized models achieved lower false‐positive rates and higher precision than the other methods for detecting causal SNPs. The common practice of prefiltering SNP lists for subsequent penalized modeling was examined and shown to substantially reduce the ability to recover the causal SNPs. Using genome‐wide SNP profiles across eight complex diseases within cross‐validation, lasso and elastic‐net models achieved substantially better predictive ability in celiac disease, type 1 diabetes, and Crohn's disease, and had equivalent predictive ability in the rest, with the results in celiac disease strongly replicating between independent datasets. We investigated the effect of linkage disequilibrium on the predictive models, showing that the penalized methods leverage this information to their advantage, compared with methods that assume SNP independence. Our findings show that sparse penalized approaches are robust across different disease architectures, producing as good as or better phenotype predictions and variance explained. This has fundamental ramifications for the selection and future development of methods to genetically predict human disease.     

Evaluating and Rejecting True Mediation Models: A Cautionary Note

This research note describes an overlooked problem in understanding whether a given variable in a model truly acts as a mediator between some exogenous variable(s) and some final dependent factor. Demonstrations of mediation and the rules for identifying have relied on simple 3-variable models with an explicit direct effects alternative model as the competing explanation. Incorporating a 4th variable demonstrates that it is quite simple to reject mediation when a true form of mediation exists. In the presence of an unobserved relation, correlated error, between mediator variable and outcome variable, the 3-variable model will consistently show direct effects when, in fact, there is no direct effect of the exogenous variable. Applying well-established rules to test for mediation in this circumstance cannot distinguish a model in which pure mediation is rejected from a model in which true mediation is correct. This poses a fundamental problem for the typical assessment of mediation offered by the Baron and Kenny procedures.     

Mediation analysis in a case-control study when the mediator is a censored variable

Mediation analysis is an approach for assessing the direct and indirect effects of an initial variable on an outcome through a mediator. In practice, mediation models can involve a censored mediator (eg, a woman's age at menopause). The current research for mediation analysis with a censored mediator focuses on scenarios where outcomes are continuous. However, the outcomes can be binary (eg, type 2 diabetes). Another challenge when analyzing such a mediation model is to use data from a case-control study, which results in biased estimations for the initial variable-mediator association if a standard approach is directly applied. In this study, we propose an approach (denoted as MAC-CC) to analyze the mediation model with a censored mediator given data from a case-control study, based on the semiparametric accelerated failure time model along with a pseudo-likelihood function. We adapted the measures for assessing the indirect and direct effects using counterfactual definitions. We conducted simulation studies to investigate the performance of MAC-CC and compared it to those of the naïve approach and the complete-case approach. MAC-CC accurately estimates the coefficients of different paths, the indirect effects, and the proportions of the total effects mediated. We applied the proposed and existing approaches to the mediation study of genetic variants, a woman's age at menopause, and type 2 diabetes based on a case-control study of type 2 diabetes. Our results indicate that there is no mediating effect from the age at menopause on the association between the genetic variants and type 2 diabetes.     

多重并行中介分析方法的比较研究

目的:介绍4种多重并行中介模型的分析方法,包括纯回归法、逆概率加权法、扩展的自然效应模型和基于权重的填补法,并对其进行探讨和比较。方法:针对多重并行中介模型,通过3种情境的模拟试验比较不同方法在不同情境下估计直接效应和间接效应的表现,并应用英国生物样本库的数据集进行实例分析。结果:模拟试验和实例分析结果显示纯回归法和逆...     

Causal mediation analysis with multiple mediators in the presence of treatment noncompliance

Randomized experiments are often complicated because of treatment noncompliance. This challenge prevents researchers from identifying the mediated portion of the intention‐to‐treated (ITT) effect, which is the effect of the assigned treatment that is attributed to a mediator. One solution suggests identifying the mediated ITT effect on the basis of the average causal mediation effect among compliers when there is a single mediator. However, considering the complex nature of the mediating mechanisms, it is natural to assume that there are multiple variables that mediate through the causal path. Motivated by an empirical analysis of a data set collected in a randomized interventional study, we develop a method to estimate the mediated portion of the ITT effect when both multiple dependent mediators and treatment noncompliance exist. This enables researchers to make an informed decision on how to strengthen the intervention effect by identifying relevant mediators despite treatment noncompliance. We propose a nonparametric estimation procedure and provide a sensitivity analysis for key assumptions. We conduct a Monte Carlo simulation study to assess the finite sample performance of the proposed approach. The proposed method is illustrated by an empirical analysis of JOBS II data, in which a job training intervention was used to prevent mental health deterioration among unemployed individuals.     

A flexible quantile regression model for medical costs with application to Medical Expenditure Panel Survey Study

Medical costs are often skewed to the right and heteroscedastic, having a sophisticated relation with covariates. Mean function regression models with low‐dimensional covariates have been extensively considered in the literature. However, it is important to develop a robust alternative to find the underlying relationship between medical costs and high‐dimensional covariates. In this paper, we propose a new quantile regression model to analyze medical costs. We also consider variable selection, using an adaptive lasso penalized variable selection method to identify significant factors of the covariates. Simulation studies are conducted to illustrate the performance of the estimation method. We apply our method to the analysis of the Medical Expenditure Panel Survey dataset.     

Confounding of indirect effects: a sensitivity analysis exploring the range of bias due to a cause common to both the mediator and the outcome

Several investigators have demonstrated that the assessment of indirect and direct effects is biased in the presence of a cause that is common to both the mediator and the outcome if one has not controlled for this variable in the analysis. However, little work has been done to quantify the bias caused by this type of unmeasured confounding and determine whether this bias will materially affect conclusions regarding mediation. The author developed a sensitivity analysis program to address this crucial issue. Data from 2 well-known studies in the methodological literature on mediation were reanalyzed using this program. The results of mediation analyses were found not to be as vulnerable to the impact of confounding as previously described; however, these findings varied sharply between the 2 studies. Although the indirect effect observed in one study could potentially be due to a cause common to both the mediator and the outcome, such confounding could not feasibly explain the results of the other study. These disparate results demonstrate the utility of the current sensitivity analysis when assessing mediation.     

The mediation proportion: a structural equation approach for estimating the proportion of exposure effect on outcome explained by an intermediate variable

It is often of interest to assess how much of the effect of an exposure on a response is mediated through an intermediate variable. However, systematic approaches are lacking, other than assessment of a surrogate marker for the endpoint of a clinical trial. We review a measure of "proportion explained" in the context of observational epidemiologic studies. The measure has been much debated; we show how several of the drawbacks are alleviated when exposures, mediators, and responses are continuous and are embedded in a structural equation framework. These conditions also allow for consideration of several intermediate variables. Binary or categorical variables can be included directly through threshold models. We call this measure the mediation proportion, that is, the part of an exposure effect on outcome explained by a third, intermediate variable. Two examples illustrate the approach. The first example is a randomized clinical trial of the effects of interferon-alpha on visual acuity in patients with age-related macular degeneration. In this example, the exposure, mediator and response are all binary. The second example is a common problem in social epidemiology-to find the proportion of a social class effect on a health outcome that is mediated by psychologic variables. Both the mediator and the response are composed of several ordered categorical variables, with confounders present. Finally, we extend the example to more than one mediator.     

Violence from parents in childhood and obesity in adulthood: Using food in response to stress as a mediator of risk

Guided by a life course perspective and concepts from models of stress and coping, this study tested the extent to which self-reported profiles of physical and psychological violence in childhood from parents were associated with greater odds of obesity in adulthood. This study also examined the extent to which adults' greater use of food in response to stress served as a mediator of potential associations of risk. Multivariate regression models were estimated using data from 1650 respondents in the 1995–2005 National Survey of Midlife in the U.S. (MIDUS). Results indicated that respondents who reported having experienced both psychological and physical violence from parents—with at least one type of violence having reportedly occurred frequently—were more likely to be classified as obese in contrast to respondents who reported never having experienced either type of violence from parents. Evidence from a sequence of models that tested mediation effects indicated that greater use of food in response to stress among respondents with problematic histories of violence explained, in part, their higher risk of adult obesity. Findings contribute to the growing body of evidence regarding psychosocial predictors of obesity, as well as the physical health consequences of childhood family violence. Results further suggest the importance of addressing particular aspects of life course social relationships—such as violence in childhood from parents—and their implications for psycho-behavioral uses of food within efforts to reduce rates of adult obesity.     

惩罚logistic回归方法在SNPs数据变量筛选研究中的应用

目的 比较L1正则化、L2正则化和弹性网三种惩罚logistic回归对SNPs数据的变量筛选能力。 方法 根据所设置的参数生成不同条件的SNPs仿真数据,利用正确率、错误率和正确指数从三个方面评价三种惩罚logistic回归的变量筛选能力。 结果 正确率表现为L2正则化惩罚logistic回归＞弹性网惩罚logistic回归＞L1正则化惩罚logistic回归;错误率表现为L2正则化惩罚logistic回归＞弹性网惩罚logistic回归＞L1正则化惩罚logistic回归;正确指数则表现为弹性网惩罚logistic回归＞L1正则化惩罚logistic回归＞L2正则化惩罚logistic回归。 结论 综合来看弹性网的筛选能力更优,弹性网融合L1、L2两种正则化的思想,在高维数据分析中既能保证模型的稀疏性,便于结果的解释,又解决了具有相关性自变量不能同时进入模型的问题。