现实队列研究中暴露与结局的时序关系

暴露在发生时间上先于结局,是队列研究的优点之一,因此在因果推断上优于其他观察性设计。本文应用有向无环图（Directed Acyclic Graphs,DAGs）构建了现实队列研究中易感人群的因果结构后发现：现实的队列研究以研究人群替换易感人群进行因果效应的估计,人群的暴露与结局在时序关系上可互为先后,因果效应估计的准确性受到替换人群的易感性和基线调查时结局识别和排除有效性的影响。     

检出偏倚:不同研究设计的结构分析

检出偏倚是一种常见的信息偏倚,在Horwitz绝经后服用雌激素与子宫内膜癌之间的关联研究中首次被提出,并广泛地存在于各类流行病学研究中。本文应用有向无环图,分析暴露-结局间的效应,并以测量的暴露-测量的结局间的关联来估计;检出偏倚的产生,实质上是从测量的暴露至测量的结局之间存在着的额外的、与研究兴趣无关的开放路径所致。通过对不同研究设计如队列研究、随机对照临床试验和病例对照研究等进行具体分析,了解检出偏倚的产生机制及其对效应估计(或关联)的可能影响。     

暴发调查设计:以急性戊型肝炎暴发为例

疾病暴发一旦确立,则意味着疾病(及其病因或疑似暴露)已经发生。暴发是疾病流行强度指标之一,虽然其本身不具备设计要素,但暴发调查可有多种设计选择。本文采用单次戊型肝炎暴发案例,调查并获得学生们对其设计类型判断的三个答案:横断面设计、病例对照设计和历史队列设计。借助因果思维和因果图,本文发现该案例并不满足或符合上述设计,即只有暴发确立型病例是已知的、可被测量两次;调查实施前暴露是未知的,暴露与结局间可混合存在着两种纵向测量时序。基于调查人群的代表性、暴发确立型病例的两次识别特征,可以将此案例视为横断面队列设计,即在假设的历史人群基础上对当前人群进行的横断面研究。对结局(和暴露)已然发生的情形,因果推断的准确性取决于其历史重建的正确性,应加强对这类基于历史重建的研究设计类型的认识。     

云端信息平台在中国国家出生队列建设与研究中的应用

大型出生队列是持续、动态地收集个体生命早期暴露信息,探讨暴露与生命远期健康结局因果关联的一种重要的队列研究类型。但由于其设计复杂、实施难度大,如何保证出生队列建设的高质量高效率是国内外流行病学研究者面临的主要挑战。2016年,国家重点研发计划资助的中国国家出生队列（China National Birth Cohort...     

基于队列研究估计暴露因素与罕见结局关联的统计方法选择

比值比(OR)和相对危险度(RR)均是评估暴露因素与研究结局间关联的常用指标, 在罕见结局的队列中, OR值常被用作RR值的近似估计, 但RR值的意义更加清晰易解释。本研究旨在基于罕见结局队列研究, 比较不同多因素回归模型获得RR与OR估计值的差别, 为基于队列研究估计暴露因素与罕见结局间关联关系时选择多因素回归方法, 以及优先报告关联大小估计指标提供参考。本研究基于中国出生队列数据开展实例研究, 以全部病种的出生缺陷为研究结局, 以受孕方式为暴露因素, 纳入孕妇年龄、是否有出生缺陷家族史等有明确证据支持的变量作为协变量, 分别拟合logistic回归、log-binomial回归以及Poisson回归, 并比较OR和RR的点估计值及其95%CI。结果表明, 在罕见结局队列研究中logistic回归估计的OR值与log-binomial回归及Poisson回归估计的RR值近似, 但log-binomial回归及Poisson回归估计的效应值更接近1.00, 且效应值的95%CI分布更窄, 但可能存在不收敛或过离散问题。针对罕见结局的队列研究, 在适用前提下, 推荐优先报告基于log-b...     

Logistic回归中误调整工具变量对因果效应估计的影响

  目的  通过统计模拟和实例数据分析,探索当存在不可观测的混杂因素时,Logistic回归分析模型中调整工具变量(instrumental variable, Ⅳ)对估计因果效应的影响。  方法  设定变量均服从二项分布,在Logistic回归分析模型中依次使用不同的参数进行统计模拟,以因果效应估计值的偏倚和标准误作为评价指标;实例数据分析是基于山东省多家医院健康体检中心的体检随访数据,以高血压为目标结局,构建纵向观察队列,筛选单核苷酸多态性(single nucleotide polymorphism, SNP)位点rs12149832作为Ⅳ,在Logistic回归分析模型中,采用不同策略(纳入/不纳入rs12149832协变量)来分析BMI与患高血压风险之间的关系。  结果  统计模拟结果显示在以Logistic回归分析模型估计暴露与结局间的效应时,协变量集中纳入Ⅳ会增大效应估计的偏倚和标准误,但增大程度较小;实例分析中,高血压队列共纳入1 240名女性,基线年龄为(37.7±10.5)岁,BMI为(22.1±3.1)kg/m2。纳入Ⅳ的模型所得的效应估计值为0.225(P＜0.001),略小于不包含Ⅳ的回归模型所得的效应估计值(0.228, P＜0.001),基本验证了关于纳入Ⅳ进行调整的统计模拟结果。  结论  观察性流行病学研究中,Logistic回归分析模型误纳入Ⅳ对效应估计值的偏倚和标准误均有影响。     

职业流行病学中的回顾性队列研究方法(待续)

职业流行病学研究较多采用回顾性队列研究。进行回顾性队列研究先按暴露情况分成暴露组与非暴露组，以过去某个时点作为观察的起始时间，并以建立队列开始到当前时间作为随防时期，回顾比较队列成员各组的发病率或死亡率，判断该因素与发病或死亡有无关联及其程度。研究的结局可为死亡、发病或其他化验功能指标，要收集足够数量的历史记录或档案资料，对暴露组和非暴露组的研究对象应以相同的方法同时间进行随访，并追踪随访到各成员的结局，即观察终点。回顾性队列研究：分析工人的死亡率常用标化死亡比(SMR)及其显著性检验，比例死亡比(PMR)，此外还包括健康工人效应(HEW)，相对标化死亡比(RSMR)、职业暴露分级死亡率(MOX)等。回顾性队列研究的常见偏倚有选择偏倚，随防偏倚，信息偏倚，混杂偏倚，应采取适当的手段来控制。尽管回顾性队列研究有一定的局限性，但仍是目前国内外研究职业危害因素致病作用的切实可行方法。     

横断面研究能否进行因果推断

基于变量调查(或测量)的共时性、统计学关联及幸存者偏倚等原因,横断面研究被认为不能进行因果推断,这是当前的"共识"。本文基于因果思维,借助因果图,首先明确定义真实截面和测量截面,并识别截面概念仅存在于理论的特性。实际横断面研究中,测量变量的共时性并不存在,而是无一例外地表现为非共时性时序,其实质上相当于测量变量间互为独立性假设,或不存在有差别错分偏倚。类似于累积病例对照研究和历史性队列研究,横断面研究均为暴露和结局已存在或发生后进行的测量,这种测量相当于对变量值的历史重建或"考古"。这类研究进行因果推断的共性前提条件之一是,测量变量与其历史变量间必须存在着因果律。测量变量均为真实变量的替代者,测量变量间的时序在因果推断上并不重要。应加强对横断面研究分析性角色的认识。     

孕期铊暴露对胎儿生长发育影响的出生队列研究

[背景]流行病学研究发现孕期铊暴露会导致不良出生结局.[目的]探讨孕期铊暴露对胎儿生长发育和出生结局的影响及铊暴露健康效应的关键窗口期.[方法]基于上海市亲子队列,共146对母子纳入本研究中.采用自制的问卷调查研究对象的人口学信息以及母婴健康数据.在孕早、中、晚期抽取孕妇血液,分娩后立刻采集新生儿脐血,通过电感耦合等离...     

长期大气二氧化氮暴露与心脑血管住院风险的因果关联研究

目的探讨长期大气二氧化氮(NO2)暴露与心脑血管住院的因果关联。方法依托我国"基于社区人群的健康队列"广州市子队列, 将2015年在广州市35个社区招募的36 271名成年参与者纳入研究, 收集基本人口学特征、生活行为方式、NO2暴露及心脑血管住院结局信息。采用基于逆概率加权的边际结构模型估计发病风险比(HR)及其95%CI。结果截至2020年12月31日共随访203 822人年, 调查对象的年龄为(50.9±17.8)岁, 心脑血管住院率为8.7%。2015-2020年, 研究对象年均NO2污染暴露为48.7 μg/m3。NO2长期暴露每增加10 μg/m3, 心脑血管、心血管及脑血管住院的HR值(95%CI)分别为1.33(1.16～1.52)、1.36(1.16～1.60)及1.25(1.00～1.55)。分层分析结果提示未婚/已婚、文化程度为初中/高中、高运动频率以及从未吸烟/当前吸烟人群可能更加易感。结论长期大气NO2暴露可增加心脑血管住院风险。     

Instrumental variables when evaluating screening trials: estimating the benefit of detecting cancer by screening.

When evaluating the benefit of detecting cancer by screening we try to answer the question, 'what would a screen detected subject's outcome have been if his/her cancer had progressed to clinical detection'. By 'outcome' we mean survival time, cancer size and stage, lead time effects and more. Because only an unethical study can answer it directly, researchers have attempted to answer the question indirectly using data from randomized cancer screening studies (subjects randomized to study (screened) or control (not screened)). Inferences are made by first selecting the cancer cohort (those subjects who are found to have cancer), then comparing subjects having screen detected cancers to subjects having clinically detected cancers. However, there are two difficulties with this approach: (i) because screening (intends to) detect cancers early, at the trial's end the study group contains more cancer cases than the control group and so the cancer cohort has some unidentified control subjects missing (that is, subjects having cancer during the screening period that have not yet been clinically detected); (ii) because screen detected cancers (may) differ from clinically detected cancers, the comparison group should include only a (non-identified) subset of the cancer cohort's control subjects (that is, only those control subjects having cancers that would have been screen detected). Statistical literature acknowledges these difficulties and attempts to solve them separately, but without success; those methods do not yield meaningful causal inferences and admit substantial bias. Recently, Angrist, Imbens and Rubin and Imbens and Rubin provide a framework for instrumental variable methods that we interpret as allowing us to make causal inferences with incompletely identified comparison groups. We apply their framework to evaluating cancer screening trials and find that we may simultaneously accommodate both difficulties while giving a meaningful answer to the question posed above. Using data from a breast cancer screening trial we demonstrate the general method with a variety of outcome measures and extensions.     

Estimating benefits of screening from observational cohort studies

Analysis and interpretation of observational studies of screening effectiveness is difficult because several biases threaten validity, including the structural healthy screenee bias, length bias, and effects of lead time. Although methods for the analysis of observational studies of screening effectiveness have been proposed, most have limitations such as incomplete control of length bias, or a heavy reliance on distributional assumptions. In this report we present a method for the analysis of observational cohort studies of screening effectiveness. Although developed independently and formulated specifically for estimating benefits of screening, our approach is implied by a more general approach developed previously by Robins. Our approach, in contrast to other available methods, avoids the healthy screenee bias, and length and lead time bias, and allows an empirical approach to analysis that need not depend highly on distributional assumptions. We illustrate application of the approach with analysis of published data from a study of breast cancer screening.     

Assessing the relative accuracies of two screening tests in the presence of verification bias

Epidemiological studies of dementia often use two-stage designs because of the relatively low prevalence of the disease and the high cost of ascertaining a diagnosis. The first stage of a two-stage design assesses a large sample with a screening instrument. Then, the subjects are grouped according to their performance on the screening instrument, such as poor, intermediate and good performers. The second stage involves a more extensive diagnostic procedure, such as a clinical assessment, for a particular subset of the study sample selected from each of these groups. However, not all selected subjects have the clinical diagnosis because some subjects may refuse and others are unable to be clinically assessed. Thus, some subjects screened do not have a clinical diagnosis. Furthermore, whether a subject has a clinical diagnosis depends not only on the screening test result but also on other factors, and the sampling fractions for the diagnosis are unknown and have to be estimated. One of the goals in these studies is to assess the relative accuracies of two screening tests. Any analysis using only verified cases may result in verification bias. In this paper, we propose the use of two bootstrap methods to construct confidence intervals for the difference in the accuracies of two screening tests in the presence of verification bias. We illustrate the application of the proposed methods to a simulated data set from a real two-stage study of dementia that has motivated this research.     

Use of the landmark method to address immortal person‐time bias in comparative effectiveness research: a simulation study

Observational comparative effectiveness and safety studies are often subject to immortal person‐time, a period of follow‐up during which outcomes cannot occur because of the treatment definition. Common approaches, like excluding immortal time from the analysis or naïvely including immortal time in the analysis, are known to result in biased estimates of treatment effect. Other approaches, such as the Mantel–Byar and landmark methods, have been proposed to handle immortal time. Little is known about the performance of the landmark method in different scenarios. We conducted extensive Monte Carlo simulations to assess the performance of the landmark method compared with other methods in settings that reflect realistic scenarios. We considered four landmark times for the landmark method. We found that the Mantel–Byar method provided unbiased estimates in all scenarios, whereas the exclusion and naïve methods resulted in substantial bias when the hazard of the event was constant or decreased over time. The landmark method performed well in correcting immortal person‐time bias in all scenarios when the treatment effect was small, and provided unbiased estimates when there was no treatment effect. The bias associated with the landmark method tended to be small when the treatment rate was higher in the early follow‐up period than it was later. These findings were confirmed in a case study of chronic obstructive pulmonary disease. Copyright © 2016 John Wiley & Sons, Ltd.     

骨关节炎的队列研究现状

骨关节炎（osteoarthritis,OA）是发生于关节的慢性退行性疾病,其患病率在风湿性疾病中居于首位,一直是困扰风湿科及骨科医师的重要难题,所以深入研究OA的危险因素和发病机制显得尤为重要,但目前国内关于OA的临床研究设计多局限于横断面研究,难以阐述疾病发生发展的因果关系。本文对国际上有关OA的队列研究文献进行总结和分析,阐述其研究内容、方法设计以及各自的特点,旨在提高对OA队列研究的重视,也为进一步开展OA队列研究提供方法参考。     

Sample size calculations for comparative studies of medical tests for detecting presence of disease

Technologic advances give rise to new tests for detecting disease in many fields, including cancer and sexually transmitted disease. Before a new disease screening test is approved for public use, its accuracy should be shown to be better than or at least not inferior to an existing test. Standards do not yet exist for designing and analysing studies to address this issue. Established principles for the design of therapeutic studies can be adapted for studies of screening tests. In particular, drawing upon methods for superiority and non-inferiority studies of therapeutic agents, we propose that confidence intervals for the relative accuracy of dichotomous tests drive the design of comparative studies of disease screening tests. We derive sample size formulae for a variety of designs, including studies where patients undergo several tests and studies where patients receive only one of the tests under evaluation. Both cohort and case-control study designs are considered. Modifications to the confidence intervals and sample size formulae are discussed to accommodate studies where, because of the invasive nature of definitive testing, true disease status can only be obtained for subjects who are positive on one or more of the screening tests. The methods proposed are applied to a study comparing a modified pap test to the conventional pap for cervical cancer screening. The impact of error in the gold standard reference test on the design and evaluation of comparative screening test studies is also discussed.     

中国母婴健康相关队列研究的文献计量分析

目的 回顾分析母婴健康队列研究的应用趋势,展望未来发展方向.方法 检索中国发表的中文及英文(简称"中英文")母婴队列研究文献,对发表时间、科研单位、研究目标与随访调查等情况进行统计分析.结果 自1991年以来,母婴健康队列研究的文献数量明显增加,中英文核心期刊群均已形成.中英文文献发表数量的年平均增长率分别为12.2％...     

目标仿真试验的基本原理、设计要素及其优缺点

队列研究往往具有研究对象代表性好、样本量大、随访时间长等独特优势,但混杂因素控制困难是其因果推断不够严密的主要障碍。相反随机对照试验（RCT）研究则在混杂因素的控制上有着绝对的优势,但其组织实施往往会受到人力、物力和伦理等方面的限制。近年来,随着真实世界队列研究数据的积累,在大型队列研究中遵照RCT研究的设计原则开展目标仿真试验（ETT）愈发受到关注。基于队列数据的ETT研究能够得到较为准确的研究结论,也为真实世界队列数据分析提供了新的思路。本文旨在介绍ETT研究的基本原理、设计要素和优缺点,以期为医学研究者开展ETT提供参考。