药物流行病学研究中具有中介作用的时依性混杂及其控制的系统综述

目的 了解药物流行病学研究中时依性混杂控制方法的应用情况。方法 系统检索英文数据库PubMed和Embase,中文数据库中国知网和万方数据知识服务平台,纳入发表于2020年6月15日以前且涉及具有中介作用的时依性混杂问题的药物流行病学研究,对其基本特征、药物暴露和结局、时依性混杂及其控制方法等问题进行分析。结果 共纳入298篇文献。涉及时依性混杂的药物流行病学研究在近年来明显增长,HIV/AIDS研究领域对该问题关注最多,占全部纳入研究的35.6%（106/298）,且其中92个研究涉及抗反转录病毒药物。最常见的研究结局为死亡,而最常关注的时依性混杂因素为实验室检查指标（179,60.1%）、合并症（136,45.6%）和合并用药（108,36.2%）。边际结构模型（MSM）和逆治疗概率加权（IPTW）是最常用的控制时依性混杂的分析方法（244,81.9%）。与合理控制时依性混杂的分析相比,传统方法调整基线混杂引起偏倚的中位数为18.2%（IQR：7.4%~40.8%）。此外,分别有28.9%和64.8%的研究对因果推断的阳性假设和无未观测混杂假设进行了检验或讨论。结论 目前大多数慢性病药物治疗领域对时依性混杂关注仍然不足。实验室检查、合并症、合并用药等在常规医疗中容易获得的指标是最普遍考虑的时依性混杂。在控制方法上,MSM和IPTW等相对较简单、结果易理解的方法最常用。     

存在时依性混杂时的G方法

目的  介绍处理时依性混杂的G方法,并对不同G方法进行探讨和比较。 方法  通过4个情境的模拟试验验证不同G方法在不同情境下对时依性混杂的处理效果,并应用英国生物样本库(UK Biobank)的数据集进行实例分析。 结果  模拟试验和实例分析结果均显示G方法能有效处理时依性混杂。模拟试验显示3种方法效果类似,G-computation易受G-null paradox的影响。随着时依性混杂因素数量增加,相比于G-computation和G-estimation,逆概率加权法(inverse probability of treatment weighting, IPTW)的效果波动较大。 结论  不同G方法都能适当地处理时依性混杂,降低统计分析过程中的偏倚大小。     

分布式循证因果数据融合方法进展

为了实现大样本量和多样化的研究人群分析,整合来自多个异质来源的数据库已经变得越来越流行。本文综述了整合多个不同人群下的不同设计的数据库在因果推理方法方面的进展。尤其是随机临床试验与外部信息相结合的研究进展以及将观察性研究和历史对照相结合的方法。此外,针对单一样本缺乏相关混杂变量信息,也可以应用两样本孟德尔随机化方法控制未知的混杂因素从而推断因果关系。这种分布式数据设计具有有效性和真实世界数据研究的安全性。     

健康指数指标筛选的因果推断方法

健康指数体系的构建与发展对于推动健康中国目标的实现具有重要的战略意义。从现实世界数据入手,通过一系列的因果推断方法,筛选和确定对健康/疾病结局具有确凿因果关系且可干预的健康指数指标,从而为健康/疾病管理提供更贴近实践、更有价值的现实世界证据是至关重要的。本文针对健康指数构建的循证医学需求,介绍了目前常用的现实世界研究中人群水平评估的因果推断方法,为健康指数指标筛选提供方法支撑。     

因果图模型及其在营养流行病学研究中的应用

不良饮食是慢性非传染性疾病最重要的可控危险因素之一,但通过随机对照试验定量阐明具体饮食因素与健康结局的因果关联面临很多困难。近年来,因果推断的迅速发展为充分利用和发掘观察性研究数据,产生高质量的营养流行病学研究证据提供了有力的理论和方法工具。其中,因果图模型通过整合大量先验知识将复杂的因果关系系统可视化,提供了识别混杂和确定因果效应估计策略的基础框架,基于不同的因果图,可选择调整混杂、工具变量或中介分析等不同的分析策略。本文对因果图模型的思想和各种分析策略的特点及其在营养流行病学研究中的应用进行介绍,旨在促进因果图模型在营养领域的应用,为后续研究提供参考和建议。     

因果思维在效应估计若干问题中的应用

流行病学是研究同质群体中“异乎寻常”的现象及其发生原因的一门科学。本文以因果思维结合其图形工具——有向无环图,围绕效应估计的若干问题——效应与关联的关系、变量及其测量版间的时序关系、动态人群自然图景、易感人群的形成、研究人群的选择、协变量和病例类型对效应估计的影响等方面,考察这种思维如何帮助我们重新认识流行病学理论、方法及应用。应加强对因果思维的认识。     

高维情况下基于倾向性评分的因果推断方法

目的 观察性研究中使用倾向性评分法估计因果效应依赖于不存在未测量混杂假设.随着大数据时代的到来,越来越多的处理前协变量可被测量并纳入分析中,使得这一假设更合理,但同时也导致标准的倾向性评分法不再适用,这就促使了高维情况下因果推断方法的发展.本研究对现有的高维情况下基于倾向性评分的因果推断方法进行概述,为实际应用提供参考...     

纵向研究中控制时依混杂的G方法

传统分析方法不能有效地控制纵向研究中的时依混杂以得到无偏因果效应估计值。本研究解释了纵向研究中正确控制时依混杂的必要性,概述了现有控制时依混杂的G方法——参数g-formula、逆概率加权和G估计,并通过比较它们的优缺点和适用情况,为研究者在纵向研究中估计因果效应提供参考。     

孟德尔随机化法在因果推断中的应用

孟德尔随机化（Mendelian Randomization,MR）研究设计,遵循“亲代等位基因随机分配给子代”的孟德尔遗传规律,如果基因型决定表型,基因型通过表型而与疾病发生关联,因此可以使用基因型作为工具变量来推断表型与疾病之间的关联。近年来,MR的研究设计随着统计学方法、大样本GWAS数据、表观遗传学以及各种“组学”技术的不断发展,在探讨复杂暴露因素与疾病结局因果关联中应用日益广泛。本文对近年来出现的MR研究设计策略、可靠性评价及局限性进行介绍。     

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。     

The parametric g-formula to estimate the effect of highly active antiretroviral therapy on incident AIDS or death

The parametric g‐formula can be used to contrast the distribution of potential outcomes under arbitrary treatment regimes. Like g‐estimation of structural nested models and inverse probability weighting of marginal structural models, the parametric g‐formula can appropriately adjust for measured time‐varying confounders that are affected by prior treatment. However, there have been few implementations of the parametric g‐formula to date. Here, we apply the parametric g‐formula to assess the impact of highly active antiretroviral therapy on time to acquired immune deficiency syndrome (AIDS) or death in two US‐based human immunodeficiency virus cohorts including 1498 participants. These participants contributed approximately 7300 person‐years of follow‐up (49% exposed to highly active antiretroviral therapy) during which 382 events occurred and 259 participants were censored because of dropout. Using the parametric g‐formula, we estimated that antiretroviral therapy substantially reduces the hazard of AIDS or death (hazard ratio = 0.55; 95% confidence limits [CL]: 0.42, 0.71). This estimate was similar to one previously reported using a marginal structural model, 0.54 (95% CL: 0.38, 0.78). The 6.5‐year difference in risk of AIDS or death was 13% (95% CL: 8%, 18%). Results were robust to assumptions about temporal ordering, and extent of history modeled, for time‐varying covariates. The parametric g‐formula is a viable alternative to inverse probability weighting of marginal structural models and g‐estimation of structural nested models for the analysis of complex longitudinal data. Copyright © 2012 John Wiley & Sons, Ltd.     

Mediation analysis of time-to-event endpoints accounting for repeatedly measured mediators subject to time-varying confounding

In this article, we will present statistical methods to assess to what extent the effect of a randomised treatment (versus control) on a time-to-event endpoint might be explained by the effect of treatment on a mediator of interest, a variable that is measured longitudinally at planned visits throughout the trial. In particular, we will show how to identify and infer the path-specific effect of treatment on the event time via the repeatedly measured mediator levels. The considered proposal addresses complications due to patients dying before the mediator is assessed, due to the mediator being repeatedly measured, and due to posttreatment confounding of the effect of the mediator by other mediators. We illustrate the method by an application to data from the LEADER cardiovascular outcomes trial.     

ECG信号分析的参数模型法

本文介绍了一种新的ＥＣＧ信号分析方法，称为参数模型法，它是用一组模型参数来表示ＥＣＧ信号，从而简化分析工作。运用本文建模方法得到的参数（Ｚ域上的一组零极点），能够很好地体现原始波形的时域特征。如果将这些参数进行聚类，它们分别将对应于时域上的Ｐ，ＱＲＳ．Ｔ波。另外，通过建模，有效地滤除了原来信号中带有的５０Ｈｚ噪声。     

观察性研究中针对未测量混杂干扰的敏感性分析方法

目的 介绍敏感性分析方法,并对不同方法进行探讨和比较。方法 通过模拟试验和实例比较混杂函数敏感性分析法和边界因子敏感性分析方法在观察性研究中校正未测量混杂因素准确性的差异。结果 模拟试验与实际例子研究结果均显示,当暴露（X）与结局（Y）之间存在未测量混杂情况下,混杂函数法和边界因子相比,在分析未测量混杂因素的效应至少达到多大强度才能导致观测效应值大小和方向彻底改变的问题上,混杂函数和边界因子分析结果相似。但混杂函数法在完全解释观测效应值时所需的混杂效应强度小于边界因子做出同样解释所需的混杂效应值。边界因子分析中设置两个参数,而混杂函数中只有一个参数,混杂函数法在分析计算过程中较边界因子法简便灵敏。结论 对于真实世界观察性研究数据,分析暴露（X）与结局（Y）之间的因果效应时,敏感性分析过程必不可少,从计算过程和结果解释上,混杂函数敏感性分析方法是一个值得推荐的方法。     

Factor analysis, causal indicators and quality of life

Exploratory factor analysis (EFA) remains one of the standard and most widely used methods for demonstrating construct validity of new instruments. However, the model for EFA makes assumptions which may not be applicable to all quality of life (QOL) instruments, and as a consequence the results from EFA may be misleading. In particular, EFA assumes that the underlying construct of QOL (and any postulated subscales or factors) may be regarded as being reflected by the items in those factors or subscales. QOL instruments, however, frequently contain items such as diseases, symptoms or treatment side effects, which are causal indicators. These items may cause reduction in QOL for those patients experiencing them, but the reverse relationship need not apply: not all patients with a poor QOL need be experiencing the same set of symptoms. Thus a high level of a symptom item may imply that a patient's QOL is likely to be poor, but a poor level of QOL need not imply that the patient probably suffers from that symptom. This is the reverse of the common EFA model, in which it is implicitly assumed that changes in QOL and any subscales cause or are likely to be reflected by corresponding changes in all their constituent items; thus the items in EFA are called effect indicators. Furthermore, disease-related clusters of symptoms, or treatment-induced side-effects, may result in different studies finding different sets of items being highly correlated; for example, a study involving lung cancer patients receiving surgery and chemotherapy might find one set of highly correlated symptoms, whilst prostate cancer patients receiving hormone therapy would have a very different symptom correlation structure. Since EFA is based upon analyzing the correlation matrix and assuming all items to be effect indicators, it will extract factors representing consequences of the disease or treatment. These factors are likely to vary between different patient subgroups, according to the mode of treatment or the disease type and stage. Such factors contain little information about the relationship between the items and any underlying QOL constructs. Factor analysis is largely irrelevant as a method of scale validation for those QOL instruments that contain causal indicators, and should only be used with items which are effect indicators.     

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.     

Loop analysis of causal feedback in epidemiology: An illustration relating to urban neighborhoods and resident depressive experiences

The causal feedback implied by urban neighborhood conditions that shape human health experiences, that in turn shape neighborhood conditions through a complex causal web, raises a challenge for traditional epidemiological causal analyses. This article introduces the loop analysis method, and builds off of a core loop model linking neighborhood property vacancy rate, resident depressive symptoms, rate of neighborhood death, and rate of neighborhood exit in a feedback network. External interventions and models including resident social isolation and neighborhood greenspace programs are hypothesized to predict different effects upon depressive symptoms and neighborhood conditions. I justify and apply loop analysis to the specific example of depressive symptoms and abandoned urban residential property to show how inquiries into the behavior of causal systems can answer different kinds of hypotheses, and thereby compliment those of causal modeling using statistical models. Neighborhood physical conditions that are only indirectly influenced by depressive symptoms may nevertheless manifest the mental health experiences of their residents; conversely, neighborhood physical conditions may be a significant mental health risk for the population of neighborhood residents. I find that participatory greenspace programs are likely to produce adaptive responses in depressive symptoms and different neighborhood conditions, which are different in character to non-participatory greenspace interventions.