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。     

Parametric g-formula方法在因果分析中的应用

目前,传统的统计学方法在控制时依性混杂等方面存在局限,本研究详细介绍了一种可调整时依性混杂的分析方法——parametric g-formula,并举例说明了实施的具体步骤,为研究者处理长期观察性数据提供了新的参考。     

基于边缘结构模型的慢性乙型肝炎抗病毒疗效评估

目的介绍边缘结构模型原理,并将该方法应用于具有时依性混杂变量的纵向数据中。方法以慢性乙型肝炎(chronic hepatitis B,CHB)抗病毒治疗的初治患者随访研究为例,根据逆概率权重构建虚拟人群解决ALT和HBV DNA的时依性混杂,拟合边缘结构模型。结果本研究所构建的边缘结构模型解决了在随访研究中时依性混杂对治疗组的影响,并有效地评估了各类CHB抗病毒药物的疗效。结论边缘结构模型基于假设条件下能无偏地估计治疗/暴露组的效应,且弥补了传统生存分析方法在时依性混杂、删失和治疗转换问题上的不足。     

边缘结构模型——一种控制时依性混杂的方法

<正>在观察性研究中,暴露或处理因素常常会随时间的变化而变化,在分析其对结局的效应时,常会受到时依性混杂因素的影响。时依性混杂因素是指同时满足以下三个条件的因素:(1)随时间变化;(2)是结局的影响因素;(3)会影响到随后的暴露/处理,同时又会受到前次暴露/处理的影响[1-2]。可见,时依性混杂因素既可以看作暴露/处理与结局的混杂因素,也可以当成暴露/处理与结局之间的一个中间变量。在估计暴     

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。     

临床预测模型中处理时依性变量的策略及进展

预测模型中考虑时依性变量可改善模型的总体表现,提高其临床应用价值。界标模型、联合模型等基于传统回归策略在处理时依性变量个数和适用情境等方面存在局限,神经网络等机器学习算法有望对其灵活处理。本文针对传统模型、机器学习算法,总结各自纳入时依性变量的建模思路,梳理各方法的适用场景,概括现有方法仍存在的问题,以期为未来预测建模处理时依性变量提供方法学启示。     

如何控制观察性疗效比较研究中的混杂因素：（一）已测量混杂因素的统计学分析方法

观察性疗效比较研究作为随机对照研究的补充,其应用价值越来越受到关注,混杂偏倚是其重要偏倚来源。本文介绍观察性疗效比较研究中已测量的混杂因素控制的统计分析方法。对于已测量的混杂因素,可采用传统的分层分析、配对分析、协方差分析或多因素分析,也可采用倾向性评分、疾病风险评分等方法进行混杂因素匹配、分层和调整。良好的设计需从源头控制各种混杂,事后统计分析则应在理解各类方法的应用前提下,严格把握适用条件。     

吸烟、饮酒与肝癌关系的条件logistic回归分析

目的 探讨吸烟、饮酒与肝癌发生的关系.方法 采用配对病例对照研究,收集福州地区388例肝癌新发病例和388例对照,对可能的危险因素进行了调整混杂因素(喝茶、乙肝和人均收入)的多因素条件logistic回归分析.结果 在控制了混杂因素后,吸烟和大量饮酒的OR值分别是2.42和2.98.吸烟与饮酒对肝癌的发生具有联合作用....     

交互作用的统计推断

假若因果推断关心的是仅仅一个风险因素对响应的因果作用的话 ,那么通过随机化试验或者通过控制混杂因素来消除混杂偏倚 ,就可以得到平均因果作用[1] 。如果因果推断包含两个或多个风险因素的话 ,除了每个因素的平均因果作用之外 ,还存在着多个风险因素之间相互的交互作用。如果研究因果意义上的交互作用 ,那么 ,同样需要进行随机化试验 ,或者控制混杂因素消除混杂偏倚。交互作用分析可以看作是多风险因素的因果推断。在流行病学研究中 ,“交互作用”是关于多风险因素分析的常用术语 ,但是 ,它被用于表示各种不同的概念。Ｒｏｔｈｍａｎ ,Ｇ…     

基于多中心数据库的观察性关联分析中残余混杂的控制与评估方法

基于健康医疗大数据的观察性研究越来越受到关注,残余混杂的控制与评估是其中亟须解决的关键问题,本文总结了多中心场景下开展关联分析的残余混杂统计学调整和敏感性分析方法。基于个体水平数据,可由各分中心使用断点回归等多种方法调整残余混杂,然后加权合并得到效应估计值;基于Meta水平数据,可采用贝叶斯Meta分析的方法获得调整后的合并效应值,也可开展残余混杂的敏感性分析,计算E值、p(q)、T(r,q)和G(r,q)。上述方法应根据适用条件及优缺点进行合理选择,如利用分中心个体数据进行残余混杂调整,通常要求严格的研究设计,并面临较高的协调成本;贝叶斯Meta分析基于部分强假设;E值等敏感性分析结果仍需经过专业的判断,以评估残余混杂风险大小。因此,利用多中心数据库开展观察性关联分析时,残余混杂的控制与评估方法仍待进一步发展和完善。     

Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men

Standard methods for survival analysis, such as the time-dependent Cox model, may produce biased effect estimates when there exist time-dependent confounders that are themselves affected by previous treatment or exposure. Marginal structural models are a new class of causal models the parameters of which are estimated through inverse-probability-of-treatment weighting; these models allow for appropriate adjustment for confounding. We describe the marginal structural Cox proportional hazards model and use it to estimate the causal effect of zidovudine on the survival of human immunodeficiency virus-positive men participating in the Multicenter AIDS Cohort Study. In this study, CD4 lymphocyte count is both a time-dependent confounder of the causal effect of zidovudine on survival and is affected by past zidovudine treatment. The crude mortality rate ratio (95% confidence interval) for zidovudine was 3.6 (3.0-4.3), which reflects the presence of confounding. After controlling for baseline CD4 count and other baseline covariates using standard methods, the mortality rate ratio decreased to 2.3 (1.9-2.8). Using a marginal structural Cox model to control further for time-dependent confounding due to CD4 count and other time-dependent covariates, the mortality rate ratio was 0.7 (95% conservative confidence interval = 0.6-1.0). We compare marginal structural models with previously proposed causal methods.     

Analyses of longitudinal, hospital clinical laboratory data with application to blood glucose concentrations

Electronic medical record (EMR) systems afford researchers with opportunities to investigate a broad range of scientific questions. In contrast to purposeful study designs, however, EMR data acquisition procedures typically do not align with any specific hypothesis. Subsequent investigations therefore require detailed characterization of clinical procedures and protocols that underlie EMR data, as well as careful consideration of model choice. For example, many intensive care units currently implement insulin infusion protocols to better control patients' blood glucose levels. The protocols use prior glucose levels to determine, in part, how to adjust the infusion rate. Such feedback loops introduce time-dependent confounding into longitudinal analyses even though they may not always be evident to the analyst. In this paper, we review commonly used longitudinal model specifications and interpretations and show how these are particularly important in the presence of hospital-based clinical protocols. We show that parameter relationships among various models can be used to identify and characterize the impact of time-dependent confounding and therefore help explain seemingly incongruous conclusions. We also review important estimation challenges in the presence of time-dependent confounding and show how certain model specifications may be more or less susceptible to bias. To illustrate these points, we present a detailed analysis of the relationship between blood glucose levels and insulin doses on the basis of data from an intensive care unit.     

The control of confounding by intermediate variables

In epidemiologic studies of the effect of an exposure on disease, the crude association of exposure with disease may fail to reflect a causal association due to confounding by one or more covariates. Most previous discussions of confounding in the epidemiologic literature have considered only point exposure studies, that is, studies that measure exposure and covariate status only once, at start of follow-up. In this paper we offer definitions of confounding suitable for longitudinal studies that obtain data on exposure, covariate, and vital status at several points in time. An important difference between longitudinal studies and point exposure studies is that, in longitudinal studies, a time-dependent covariate can be simultaneously a confounder and an intermediate variable on the causal pathway from exposure to disease. In this paper I propose an estimator, the extended standardized risk difference, that provides control for confounding by a covariate that is simultaneously a confounder and an intermediate variable.     

Marginal structural models and causal inference in epidemiology

In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also affected by previous treatment. This paper introduces marginal structural models, a new class of causal models that allow for improved adjustment of confounding in those situations. The parameters of a marginal structural model can be consistently estimated using a new class of estimators, the inverse-probability-of-treatment weighted estimators.     

Estimating the effects of time-varying treatments: incidence of fractures among postmenopausal Japanese women

BACKGROUND: In the absence of unmeasured confounding, standard methods for estimating the effects of time-varying treatments on an event are biased when a time-dependent risk factor for the event also predicts subsequent treatments and when past treatment history predicts subsequent risk factor levels. In contrast, structural models provide unbiased estimates of effects when unmeasured confounding is absent. METHODS: We describe a multiplicative structural mean model and use it to estimate the effects of time-varying osteoporosis treatments on incidence of fractures among 1328 postmenopausal women over 40 years of age in a hospital-based cohort study in Japan. The parameters of the structural mean model are estimated by g-estimation. RESULTS: The number of vertebral fractures and bone mineral density levels predicted the selection of subsequent treatments and were affected by the previous treatments. Incidence rate ratios of bisphosphonates, active vitamin D3, and conjugated estrogen compared with no drug therapy were 0.58 (95% confidence interval = 0.44-0.77), 0.82 (0.48-1.38), and 0.60 (0.47-0.76), respectively, after adjusting time-dependent confounders. For initial treatments estimated by the standard Poisson-GEE, incidence rate ratios were 1.61 (1.23-2.10), 1.16 (0.96-1.40), and 0.73 (0.52-1.02), respectively. CONCLUSIONS: Our analysis using the structural mean model showed that bisphosphonates, active vitamin D3, and conjugated estrogen all had preventive effects on the incidence of fractures by appropriate adjustments for time-dependent confounders. The results from standard Poisson-GEE analysis were the opposite of these results and of evidence from randomized trials. We consider our methods useful to estimate time-varying treatments within observational data.     

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.     

Bias in observational research: 'confounding'

Confounding is an important and common issue in observational (non-randomized) research on the effects of pharmaceuticals or exposure to etiologic factors (determinants). Confounding is present when a third factor, related to both the determinant and the outcome, distorts the causal relation between these two. There are different methods to control for confounding. The most commonly used are restriction, stratification, multivariable regression models, and propensity score methods. With these methods it is only possible to control for variables for which data is known: measured confounders. Research in the area of confounding is currently directed at the incorporation of external knowledge on unmeasured confounders, the evaluation of instrumental variables, and the impact of time-dependent confounding.     

Utility of time-dependent inverse-probability-of-treatment weights to analyze observational cohorts in the intensive care unit

Objective

When analyzing observational databases, marginal structural models (MSMs) may offer an appealing approach to estimate causal effects. We aimed at evaluating MSMs, in accounting for confounding when assessing the benefit of intensive care unit (ICU) admission and on its interaction with patient age, as compared with propensity score (PS) matching.

Study Design and Setting

PS and inverse-probability-of-treatment weights for MSMs were derived from an observational study designed to evaluate the benefit of ICU admission on in-hospital mortality. Only first ICU triages (time-fixed weights) or whole triage history (time-dependent weights) were considered. Weights were stabilized by either the prevalence of the actual treatment or the probability of the actual treatment given baseline covariates. Risk difference (RD) was the main outcome measure.

Results

MSMs with time-dependent weights offered the best reduction in the baseline imbalances as compared with PS matching. No effect of ICU admission on in-hospital mortality was found (RD = 0.010; 95% confidence interval = −0.038, 0.052) with no interaction between age and treatment.

Conclusion

MSMs appear interesting to handle selection bias in observational studies. When confounding evolves over time, the use of time-dependent weights should be stressed out.     

Expert knowledge,confounding and causal methods

The absence of unmeasured confounding is the fundamental condition for causal inference from observational data. Even when this condition holds and the models are correctly specified, estimates from standard methods may not have a causal interpretation if there are time-dependent confounders that are affected by prior exposure. Causal methods, such as marginal structural models and structural nested models, avoid this problem.