效应修饰作用分析方法与案例解析系列（一）：流行病学和经典Meta分析中的效应修饰

本文简要介绍了流行病学研究中效应修饰作用的定义、分类、特征及其研究意义，阐述效应修饰作用因子与混杂因素的区别与联系，并分别分析了效应修饰作用在流行病学原始研究及Meta分析中对结果的影响，通过图表展示了Meta分析中效应修饰作用可能出现的场景及其对应决策，旨在引起研究者对效应修饰作用的重视。本文通过“钠-葡萄糖协同转运蛋白2抑制剂治疗2型糖尿病疗效”这一实际案例展示经典Meta分析中如何识别和处理效应修饰作用，分别展示了亚组分析和Meta回归2种方法的分析思路和结果解读，并总结2种方法在探讨效应修饰作用方面的优缺点及注意事项，为未来研究者的方法选择提供参考。     

效应修饰作用分析方法与案例解析系列（三）：个体数据Meta分析中的效应修饰

简要介绍个体数据Meta分析在效应修饰作用方面的独特优势、整体分析思路及现有分析方法,除了常见的Meta回归和亚组分析外,还介绍了利用部分个体数据合并集合水平数据的分析方法,并总结以上方法的报告现状。以“钠-葡萄糖协同转运蛋白2抑制剂对2型糖尿病患者SBP的影响”作为案例,分别展示上述方法在个体数据Meta分析中的实际应用及结果解读,总结各方法的优势和局限性。     

Stata软件在网状Meta分析中的应用

Stata软件是目前唯一一款在多变量框架下使用频率学派进行网状Meta分析的软件,其操作简单、便捷且具有良好的扩展性,可以较为方便地应用于网状Meta分析中。Stata中network包集成效果好,特别适合于没有共同对照组或对照组有较多缺失数据时的网状Meta分析,且方法过程简单。本文通过实例演示了使用Stata的network包来进行网状Meta分析。     

纬度对高温与老年人死亡效应关系修饰作用的Meta分析

目的 探讨高温对老年人死亡风险的影响,并分析纬度对高温与死亡风险的效应修饰作用.方法 收集国外高温与老年人死亡风险关系的研究,采用Meta分析方法合并效应值,再通过Meta回归模型进一步分析纬度对该关系的修饰作用.结果 共纳入7篇文章,共14个城市.高温天气温度每升高1℃,老年人死亡风险增加4.3％(95％ CI:2.4％～6.3％).纬度0 °～29°、30°～39°、40°～49°、≥50°时温度每升高1℃,老年人死亡风险分别增加2.5％(95％CI:2.2％～2.9％),3.1％(95％CI:2.1％～4.2％),6.0％(95％ CI:-0.9％～13.3％),6.0％(95％CI:5.1％～7.0％);纬度每升高5°,老年人死亡风险增加0.2％(95％CI:0.1％～1.7％).结论 高温会增加老年人群的死亡风险,中高纬度地区在高温天气时老年人死亡风险更大.     

5种不同质子泵抑制剂的三联疗法根除幽门螺杆菌效果比较的网状Meta分析

目的 应用网状Meta分析方法分析基于5种不同质子泵抑制剂的标准三联疗法根除幽门螺杆菌的价值。方法 检索PubMed、MEDLINE、Embase、Cochrane Library,按预先制定的纳入排除标准进行筛选,并对纳入RCT的质量进行质量评价,数据采用WinBugs软件进行数据分析。结果 共纳入5种三联疗法,19个RCT,共4 660名患者,网状Meta分析结果显示:埃索美拉唑三联疗法和奥美拉唑三联疗法、兰索拉唑三联疗法、泮托拉唑三联疗法之间的差异有统计学意义,雷贝拉唑三联疗法和奥美拉唑三联疗法和泮托拉唑三联疗法之间的差异有统计学意义。 结论 基于网状Meta分析和排序结果,埃索美拉唑三联疗法和雷贝拉唑三联疗法的根除率相对其他3种疗法高。     

Meta分析在医院所有权与医疗服务质量关系研究中的应用

对Meta分析在医院所有权与医疗服务质量关系研究中的应用进行综述，阐述Meta分析应用于该领域的具体内容，并探索存在的问题，最后提出推广Meta分析在医疗卫生服务领域中应用的建议。     

幽门螺旋杆菌药敏试验指导的治疗方案和新一线四联方案:网状Meta分析

目的 比较细菌培养药敏试验指导的幽门螺旋杆菌治疗方案和新一线4种方案（伴同疗法、混合治疗、序贯治疗、铋剂四联治疗）的疗效和不良反应，并进行概率排序为临床治疗提供科学依据。方法 系统检索电子数据库，筛选截至2015年6月比较细菌培养药敏试验指导治疗和新一线4种方案，包括基于细菌培养药敏试验指导治疗、伴同疗法、混合治疗、序贯治疗、铋剂四联治疗方案的随机对照试验。通过网状Meta分析和单组率的Meta分析，定量综合这些方案的相对和绝对疗效及不良反应。采用Jadad评分量表评价随机对照试验的方法学质量，构建漏斗图定性测量发表偏倚，其中不对称性检验使用Egger线性回归法或者Begg秩相关法。结果 共纳入20个初治幽门螺旋杆菌感染的随机对照试验，涉及6 753例患者。网状Meta分析表明，细菌培养药敏试验指导治疗的方案显著优于其他方案，根除率最高，不良反应发生率最低；伴同疗法虽然高效但最有可能出现不良反应事件；混合治疗和铋剂四联治疗方案具有较高的疗效、较低的不良反应发生率。结论 对于成年人幽门螺旋杆菌感染，细菌培养药敏试验指导的治疗方案显著优于伴同疗法、混合治疗、铋剂四联治疗和序贯治疗方案；医疗条件匮乏地区可选择混合治疗和铋剂四联治疗方案。     

Meta分析方法及其医学科研价值与评价

目的：评价Meta分析（Meta－analysis)在医学科研中的作用和价值。方法：论述Meta分析的基本步骤，统计分析方法及其应用于医学科研的优点和缺点并对Meta分析与临床试验等其他研究方法进行了比较，结果：Meta分析是多个同类研究和大量文献综合分析的重要方法，是循证医学（evidence-based medicine)获取，评价和应用最佳证据的重要手段，也可作为医学科研项目管理评介的方法之一。但是，Meta分析属于描述性二次分析，存在混杂偏倚，文献报道偏倚以及分析方法本身的一些缺点，结论：应该正确认识和合理应用Meta分析方法。     

Meta回归与亚组分析在异质性处理中的应用

探讨Meta回归与亚组分析在异质性的识别与处理中的应用及意义.利用文献提供的二次数据建立Meta回归模型,筛选出异质性的影响因素,根据该因素做亚组分析,并比较亚组分析前后异质性的变化.Meta分析资料经异质性检验,Q=44.71,df=27,P=0.017,认为存在异质性.经Meta回归分析,从可能导致异质性的因素(研究时间、地区、样本量、病例对照比值等)中筛选出样本含量为异质性因素(P=0.012)、地区为可能的异质性因素(P=0.091).然后进行亚组分析,异质性明显减小(∑Q 由44.71减小至32.11).结论 :Meta回归法对筛选异质性影响因素比较简便可靠,据此进行的亚组分析能明显降低亚组内的异质性.故存在统计学异质性又要计算合并效应时推荐二者结合使用,可正确识别并降低异质性,从而使Meta分析结果更为稳健与合理.     

Meta分析对效应量率的合并方法的研究进展

Meta分析作为系统评价的一种主要方法,目前主要运用于对各种临床随机对照试验的研究结果比值比(OR)、相对比(RR)等效应量的合并分析,而当各独立研究的效应量为率时,却没有比较成熟的分析方法。本文就目前利用Meta分析的方法对效应量率合并的几种计算方法的研究进展情况作一综述。     

母乳喂养对生命早期危险因素与儿童肥胖关联的修饰作用研究进展

儿童肥胖已成为全球共同关注的公共健康问题，其发生发展与生命早期危险因素暴露密切相关。近年来，越来越多流行病学研究证据显示，母乳喂养对儿童肥胖的生命早期危险因素如肥胖高遗传风险、母亲患妊娠糖尿病、出生巨大儿等因素带来的健康危险效应具有修饰作用。本文通过对该领域现有的研究结果进行综述，总结母乳喂养对儿童肥胖遗传及生命早期环境危险因素的修饰作用，为明确母乳喂养对特定高危儿童的肥胖防控效益和实施针对性的防控策略提供参考。     

Effect modification by time-varying covariates

Marginal structural models (MSMs) allow estimation of effect modification by baseline covariates, but they are less useful for estimating effect modification by evolving time-varying covariates. Rather, structural nested models (SNMs) were specifically designed to estimate effect modification by time-varying covariates. In their paper, Petersen et al. (Am J Epidemiol 2007;166:985-993) describe history-adjusted MSMs as a generalized form of MSM and argue that history-adjusted MSMs allow a researcher to easily estimate effect modification by time-varying covariates. However, history-adjusted MSMs can result in logically incompatible parameter estimates and hence in contradictory substantive conclusions. Here the authors propose a more restrictive definition of history-adjusted MSMs than the one provided by Petersen et al. and compare the advantages and disadvantages of using history-adjusted MSMs, as opposed to SNMs, to examine effect modification by time-dependent covariates.     

Four types of effect modification: a classification based on directed acyclic graphs

It is possible to classify the types of causal relationships that can give rise to effect modification on the risk difference scale by expressing the conditional causal risk-difference as a sum of products of stratum-specific risk differences and conditional probabilities. Directed acyclic graphs clarify the causal relationships necessary for a particular variable to serve as an effect modifier for the causal risk difference involving 2 other variables. The directed acyclic graph causal framework thereby gives rise to a 4-fold classification for effect modification: direct effect modification, indirect effect modification, effect modification by proxy and effect modification by a common cause. We briefly discuss the case of multiple effect modification relationships and multiple effect modifiers as well as measures of effect other than that of the causal risk difference.     

History-adjusted marginal structural models for estimating time-varying effect modification

Much of epidemiology and clinical medicine is focused on estimating the effects of treatments or interventions administered over time. In such settings of longitudinal treatment, time-dependent confounding is often an important source of bias. Marginal structural models (MSMs) are a powerful tool for estimating the causal effect of a treatment using observational data, particularly when time-dependent confounding is present. In recent statistical work, van der Laan et al. presented a generalized form of MSMs called "history-adjusted" MSMs (Int J Biostat 2005;1:article 4). Unlike standard MSMs, history-adjusted MSMs can be used to estimate modification of treatment effects by time-varying covariates. Estimation of time-dependent causal effect modification is frequently of great practical relevance. For example, clinical researchers are often interested in how the prognostic significance of a biomarker for treatment response can change over time. This article provides a practical introduction to the implementation and interpretation of history-adjusted MSMs. The method is illustrated using a clinical question drawn from the treatment of human immunodeficiency virus infection. Observational cohort data from San Francisco, California, collected between 2000 and 2004, are used to estimate the effect of time until switching antiretroviral therapy regimens among patients receiving a non suppressive regimen and how this effect differs depending on CD4-positive T-lymphocyte count.     

基于复杂网络理论的健康相关行为与慢性非传染性疾病关系研究进展

近年来,有关健康相关行为（健康行为）与慢性非传染性疾病（慢性病）的研究视角从专注多种健康行为与某个慢性病间“多因单果”的独立效应,逐渐转向多个健康行为与慢性病“多因多果”的复杂相互作用。复杂网络理论是系统科学的重要板块,该理论考虑了众多因素在同一网络中的博弈关系,可通过一系列复杂网络模型、指标,揭示健康行为与慢性病多个因素间如何相互作用。本文对复杂网络理论定义、发展及其在健康行为与慢性病领域的常用模型和指标、案例内容进行综述,旨在打开复杂网络理论在健康领域更广阔的运用场景,为后续健康行为与慢性病关系的研究提供可参考的思路和工具。     

Recommendations for presenting analyses of effect modification and interaction

Authors often do not give sufficient information to draw conclusions about the size and statistical significance of interaction on the additive and multiplicative scales. To improve this, we provide four steps, template tables and examples. We distinguish two cases: when the causal effect of intervening on one exposure, across strata of another factor, is of interest ('effect modification'); and when the causal effect of intervening on two exposures is of interest ('interaction'). Assume we study whether X modifies the effect of A on D, where A, X and D are dichotomous. We propose presenting: (i) relative risks (RRs), odds ratios (ORs) or risk differences (RDs) for each (A, X) stratum with a single reference category taken as the stratum with the lowest risk of D; (ii) RRs, ORs or RDs for A within strata of X; (iii) interaction measures on additive and multiplicative scales; (iv) the A-D confounders adjusted for. Assume we study the interaction between A and B on D, where A, B and D are dichotomous. Steps (i) and (iii) are similar to presenting effect modification. (ii) Present RRs, ORs or RDs for A within strata of B and for B within strata of A. (iv) List the A-D and B-D confounders adjusted for. These four pieces of information will provide a reader the information needed to assess effect modification or interaction. The presentation can be further enriched when exposures have multiple categories. Our proposal hopefully encourages researchers to present effect modification and interaction analyses in as informative a manner as possible.     

大豆异黄酮对低密度脂蛋白氧化修饰的抑制作用

为探讨大豆异黄酮 (SI)在体外及体内对低密度脂蛋白 (LDL)氧化修饰的作用特点及其与α 生育酚的异同 ,采用密度梯度超速离心法分离制备血清LDL后 ,建立了Cu2 + 诱导LDL氧化修饰反应模型 ,体外向模型中直接加入SI和α 生育酚后 ,通过监测反应体系中硫代巴比妥酸反应物质 (TBARS)和共轭双烯生成量的变化观察它们的作用。体内实验通过先补加SI于高脂饲料喂饲大鼠 ,再测定血中LDL对Cu2 + 诱导氧化的敏感性以反映SI的效果。结果表明 ,体外无论在启动LDL氧化反应前还是反应后加入SI,均显著降低体系中TBARS和共轭双烯的生成 ,并呈现剂量相关关系 ;仅在启动LDL氧化反应前加入α 生育酚显示良好的抑制效果 ,当启动LDL氧化反应后加入α 生育酚则未表现任何防护作用。在大鼠喂饲实验中 ,高脂饲料导致动物LDL对氧化修饰的敏感性增加 ,补加SI对此有明显的拮抗作用。可见 ,在体外SI是与α 生育酚作用有所区别的一类天然抗氧化剂 ,它在体外和体内均能显著抑制LDL的氧化修饰     

Semiparametric regression models for detecting effect modification in matched case-crossover studies

In matched case-crossover studies, it is generally accepted that covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model because any stratum effect is removed by the conditioning on the fixed number of sets of a case and controls in the stratum. Hence, the conditional logistic regression model is not able to detect any effects associated with the matching covariates by stratum. In addition, the matching covariates may be effect modification and the methods for assessing and characterizing effect modification by matching covariates are quite limited. In this article, we propose a unified approach in its ability to detect both parametric and nonparametric relationships between the predictor and the relative risk of disease or binary outcome, as well as potential effect modifications by matching covariates. Two methods are developed using two semiparametric models: (1) the regression spline varying coefficients model and (2) the regression spline interaction model. Simulation results show that the two approaches are comparable. These methods can be used in any matched case-control study and extend to multilevel effect modification studies. We demonstrate the advantage of our approach using an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis associated with drinking water turbidity.