中介分析方法及在其医学研究中的应用

<正>目前,中介分析已经被广泛应用于心理、预防、流行病等的医学研究~([1]),与传统的多元分析方法不同,中介分析不仅需要分析自变量与因变量之间的关系,还需要对自变量的不同作用加以分析,在一定程度上揭示自变量对因变量影响的内在机制,是一种因果效应推断的方法,已经越来越受到医学科研工作者的关注。本文阐述目前中介分析的主要方法及在医学研究中的应用。中介分析模型中介分析的基本模型如图1所示,X、M、Y分别表     

因果中介效应分析及SAS实现

目的 通过模拟数据，研究父母提供的鼓励环境是否会对儿童的认知发展产生影响，探索父母鼓励是否增强了孩子的学习动机，介绍因果中介效应分析方法的原理及SAS实现。方法 在未控制混杂因素和控制混杂因素两种情况下，运用因果中介效应分析方法对鼓励和认知得分之间的因果路径进行分解，确定中介变量动机在因果关系中的作用程度。结果 学习动机在父母鼓励与儿童认知发展之间起中介作用，中介效应占总效应的比例为47%(不控制混杂因素)、37%(控制混杂因素)。结论 学习动机是中介变量，父母鼓励可以通过增强孩子的学习动机来提高儿童的认知发展。在满足相关前提和假设下，CAUSALMED过程可以实现因果中介效应分析，探索因果关系的内在影响机制。     

医生全科职业认知与择业意愿——收入预期与职业认同的中介效应

本研究以山东省某市正在求职的医生为研究对象,探索其全科职业认知影响职业意愿的路径和程度。构建全科认知和职业意愿分析框架,对医生随机抽样,以职业认知为自变量,以职业认同和收入预期为中介变量,以职业意愿为因变量,运用因子分析,形成因子得分,运用结构方程模型进行分析检验。实证结果表明:职业认知影响收入预期,进而影响职业认同,最终对职业意愿的间接效应也显著。实践中,政府制定政策时,应考虑政策对医生职业认知的作用。     

基于潜变量增长曲线中介模型的帕金森病患者日常生活能力的研究

目的 分析帕金森病（PD）患者自主神经功能障碍在快速眼动睡眠行为障碍（RBD）和日常生活能力之间的纵向中介作用及其随时间的变化趋势。方法 本研究纳入帕金森进展标志物计划（PPMI）数据库410例PD患者资料,分别采用快速眼动睡眠行为障碍筛查量表（RBDSQ）、自主神经症状量表（SCOPA-AUT）和帕金森病统一评价量表第二部分（UPDRS II）评估RBD、自主神经功能障碍和日常生活能力。以RBD为自变量,自主神经功能障碍为中介变量,日常生活能力为因变量构建潜变量增长曲线中介模型。采用全息极大似然估计进行参数估计,偏差校正的非参数百分位Bootstrap法进行中介效应检验,Chi-Square(df)、非规范拟合指数（Tucker Lewis index,TLI）、比较拟合指数（comparative fit index,CFI）指标来评价模型的拟合度。结果 相关分析显示,RBD、自主神经功能障碍、日常生活能力六次随访两两之间均呈正相关（P<0.01）。潜变量增长曲线中介模型结果显示,快速眼动睡眠障碍的截距→日常生活能力的截距的总效应显著（β=0.427,95%CI:0.310～...     

基于有向无环图和线性回归理论定量分析常见混杂的影响

目的结合有向无环图(directed acyclic graphs, DAGs)与线性回归模型,提出常见混杂的定量分析方法。方法针对典型的两种DAGs(情形1:X←C→Y;情形2:X←C_1→M←C_2→Y),基于线性回归理论给出偏倚大小的定量表达式,并探讨各参数对因果效应估计的影响。结果对于情形1的DAG,暴露X与结局Y的因果效应估计需通过关闭二者间后门路来控制混杂C,否则X与Y间因果估计会受到混杂偏倚的影响。理论推导结果显示,若只改变C对X的效应则同时会增加X的方差,此效应的强弱对混杂偏倚的影响是非单调的,除非在改变C与X的效应的同时控制X的方差不变,而C与Y间的效应强弱对混杂偏倚的影响则是单调的。对于情形2,又称M-DAGs无需做变量控制即可做X与Y的因果估计,但当错误地控制碰撞点M后会导致后门路径打开而出现混杂,此时需进一步控制C_1和(或)C_2来关闭后门路径。我们用回归理论解释了该结果,并且得到当C_1与X相关性较高时,同时控制C_1和M的方法结果会不稳定。结论本研究基于有向无环图,根据线性回归理论推导出常见混杂的定量分析方法,该方法也适用于无法观测的混杂,为多因素因果推断提供了一种实用工具。     

logistic回归中自变量相对重要性的相对权重估计

目的将相对权重指标扩展应用于logistic回归分析,以更精确评价自变量的相对重要性。方法原始变量通过最小二乘正交变换获得一组独立不相关但与原变量最大相关的新变量集,并对因变量关于新变量集作回归分析获取一组标准回归系数β,再通过分析正交变量对原变量的回归作用返回至原变量集获取一组相关系数λ,最后对这两组估计参数平方乘积和所得结果就是自变量成比例贡献于因变量的重要性。结果相对权重总和等于模型的总变异R2,有效地分配了每个自变量对因变量的贡献大小。结论当存在共线性问题时,相对权重是评价自变量相对重要性的精确量化指标,为许多分类资料分析中希望确定自变量相对重要性的研究者提供一个可行的估计方法 。     

偏相关系数和偏回归系数的统计解析与意义

本研究提出了一个解析在多元线性回归时每一个自变量与因变量之间的关系的偏相关与偏回归系数的方法，包括系数的估计和每一个自变量与因变量之间的散点图的绘制。使我们能够在多元的情况下也能象一元回归一样，作出变量间关系的散点图，从而达到深入了解回归分析结果的可靠性和协助进行回归诊断的目的。     

自我效能感在应激和抑郁之间的中介效应和调节效应分析

目的通过引入中介效应和调节效应的概念来探讨自我效能感在应激和抑郁之间的作用。方法应用青少年生活事件量表、一般自我效能感量表和Beck抑郁自评量表等对732名大学生进行测评,利用回归分析来研究变量之间的效应关系。结果自我效能感在应激和抑郁之间的部分中介效应显著,中介效应与总效应之比为=0.0961,中介效应与直接效应之比为=0.1063。交互作用项"自我效能感×应激"的回归系数在以抑郁为因变量的回归方程中达到显著性水平(β=-0.108,t=-3.164,P=0.002),且引入交互作用项后新增解释量(△R2)亦达到显著性水平(△R2=0.012,P=0.002)。结论自我效能感在应激和抑郁的关系中起中介效应和调节效应。     

生存结局的多中介变量的中介分析方法比较

目的本研究以生存结局为切入点,探讨含两个中介变量时的中介生存分析模型(Aalen相加风险模型、Cox比例风险模型、加速失效时间AFT模型),为预后的多中介变量分析方法的选择提供应用建议。方法通过统计模拟试验,设定不同的相关系数、效应比、删失率等,从第一类错误及检验效能等方面对上述三种方法进行统计学性质评价。结果中介变量与暴露的相关系数越大,越容易发现中介变量的中介效应;删失率与效应比对Aalen模型的影响较大,对其他两种模型的影响较小;随着删失率的降低,Aalen模型的第一类错误反而膨胀,故Aalen模型不适用于多中介变量的分析;样本量越大,三种模型的检验效能差别减小且趋于稳定。不同参数设定下,AFT模型的检验效能最大,其次为Cox模型,最后为Aalen模型。结论 AFT模型优于其他两种方法,推荐用于生存结局的多中介变量的中介分析;进行中介分析时需要足够的样本量。     

单参数指数模型在医药数据分析中的应用研究

对于因变量Y是0-1变量,当自变量xi是连续型变量的情况,logistic回归模型长期被应用,来解决这类问题,随着非参数和半参数模型的发展以及计算机编程水平的提高,我们可以用非参数模型或者半参数模型来解决这类问题.非参数模型或者半参数模型可以直接建立Y与xi之间的关系,这种关系的函数的具体的表达式未知,但是,我们可以通过计算机软件得出这个函数的估计值,得到预测模型,对于给定的值就可求出预测值.如果这种方法计算的结果与logistic回归模型预测的结果一致,或者比logistic回归模型预测的结果好,可提供一种新的解决因变量Y是0-1变量,xi是连续型变量的关系模型,这种关系模型比logistic回归模型好,能表示出Y与置的直接的函数关系.本文的单参数指数模型就是半参数模型中的一种,目的是说明单参数指数模型的可行性和优越性.     

Estimation of the mediation effect with a binary mediator

A mediator acts as a third variable in the causal pathway between a risk factor and an outcome. In this paper, we consider the estimation of the mediation effect when the mediator is a binary variable. We give a precise definition of the mediation effect and examine asymptotic properties of five different estimators of the mediation effect. Our theoretical developments, which are supported by a Monte Carlo study, show that the estimators that account for the binary nature of the mediator are consistent for the mediation effect defined in this paper while other estimators are inconsistent. We use these estimators to study the mediation effect of chronic cerebral infarction in the causal relationship between the apolipoprotein E epsilon4 allele and cognitive function among 233 deceased participants from the Religious Orders Study, a longitudinal, clinical-pathologic study of aging and Alzheimer's disease.     

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.     

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.     

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.     

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.     

Penalized models for analysis of multiple mediators

Mediation analysis attempts to determine whether the relationship between an independent variable (e.g., exposure) and an outcome variable can be explained, at least partially, by an intermediate variable, called a mediator. Most methods for mediation analysis focus on one mediator at a time, although multiple mediators can be jointly analyzed by structural equation models (SEMs) that account for correlations among the mediators. We extend the use of SEMs for the analysis of multiple mediators by creating a sparse group lasso penalized model such that the penalty considers the natural groupings of parameters that determine mediation, as well as encourages sparseness of the model parameters. This provides a way to simultaneously evaluate many mediators and select those that have the most impact, a feature of modern penalized models. Simulations are used to illustrate the benefits and limitations of our approach, and application to a study of DNA methylation and reactive cortisol stress following childhood trauma discovered two novel methylation loci that mediate the association of childhood trauma scores with reactive cortisol stress levels. Our new methods are incorporated into R software called regmed.     

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.     

Primary caregivers of cancer patients in the palliative phase: a path analysis of variables influencing their burden

Research has shown that several variables influence the burden of primary caregivers of cancer patients staying at home in the palliative phase, but the associations between these variables have hardly been explored. The aim of this study was to examine the associations of theory-driven variables with the caregivers' burden by means of path analysis. The sample consisted of 96 caregivers of cancer patients in the palliative phase staying at home recruited from a hospital trust in Norway. The dimensions of burden from the Caregiver Reaction Assessment, namely self esteem, lack of family support, impact on finances, and impact on daily schedule, were used as the dependent variable. The following independent variables were tested in the models: the patients' levels of pain, fatigue, and nausea; and the caregivers' physical quality of life, anxiety and depression, and social support. The Partial Least Squares approach to structural equation modelling was used for the path analysis. Model 1 shows the direct associations between the independent variables and the dependent variable, explaining 16% of the variance in caregiver burden. Model 1 supports the finding that only caregivers' depression has a direct significant association with caregiver burden, and shows further that the effects of the other independent variables on burden are mediated through depression. In Model 2, anxiety and depression are mediating factors between three other independent variables and caregiver burden, and 12% of the variance is explained. Model 2 supports none of the independent variables as antecedents of burden. Testing of the models suggested that caregivers' depression was the main factor associated with caregiver burden, but also an important mediator of indirect associations of indirect associations of caregivers' anxiety and physical health.     

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.     

Important Considerations in Conducting Statistical Mediation Analyses

An investigator wishes to examine mediation in a randomized control trial of the effectiveness of an intervention, which consists of a computerized decision aid for promoting colorectal cancer screening. Mediation is a naturally occurring process, and in any given instance, research investigators seek to ascertain whether it has occurred. In the case of a prevention intervention for a specific chain of events, mediation occurs (1) when the prevention intervention effects a change on a targeted intermediate condition: a mediator, for example, a person’s intentions to get a colorectal screening examination; and (2) when, at a later point in time, this condition effects a change on a targeted outcome, for example, the actual behavior of getting a colorectal screening exam. Full mediation is said to occur when the effectiveness of the intervention on the targeted outcome only takes place through the intermediate condition and does not directly affect the targeted outcome. Partial mediation is said to occur when the intervention causes changes in both the intermediate condition and the targeted outcome. The assessment of mediation is important because conclusions about the efficacy of a public health intervention may depend on how these indirect influences are assessed.Open in a separate windowOpen in a separate windowThe proposed study regarding the effectiveness of this aforementioned decision aid would include 200 individuals who will be randomized to a decision aid (intervention) condition or to a usual care (control) condition. Thus, the group variable that contains these 2 conditions serves as the independent variable. The researcher hypothesizes that the independent variable will cause changes in a mediator variable (e.g., increased intentions to get screened for colorectal cancer). The mediator variable is then hypothesized to cause changes in the outcome (the dependent variable), which is the actual screening behavior that would occur at a later time point.