基于个体的标准化法--倾向评分加权

倾向评分加权是利用倾向评分值对每个观察单位进行加权调整.由于倾向评分将许多协变量综合为一个变量,因此通过倾向评分加权可以使各混杂变量在两组人群中的分布趋于一致.根据调整后标准人群的不同分为两种加权方法:逆处理概率加权法(IPTW)和标准化死亡比加权法(SMRW).本文实例分析表明,用IPTW和SMRW加权调整后处理组和对照组妇女各混杂变量的分布均趋于一致,两种方法调整后的效应估计基本相同.本文介绍倾向评分加权法的基本原理、具体方法,并结合实例探讨了其在流行病学中的应用.     

用SPSS宏程序实现观察对象的倾向评分配比

倾向评分配比(propensity score matching,PSM)是平衡观察性研究中混杂因素的有效方法,越来越多的研究者将其应用于流行病学的设计和分析阶段〔1,2〕。但在统计软件中没有现成的模块进行PSM过程,需要编制宏程序来实现。本文结合实例介绍用SPSS宏程序实现PSM的过程。     

匹配在观察性研究中的作用——有向无环图视角

匹配是观察性研究中选择研究对象的一种常用方法,具有控制混杂因素、提高统计效率等作用,但其控制混杂因素的作用在不同观察性研究中并不一致,匹配在队列研究中能够消除匹配变量的混杂偏倚,但在病例对照研究中匹配本身并不能消除混杂偏倚。在匹配性病例对照研究选择匹配变量时,研究者可能并不能准确判断该变量是否为混杂变量,若误将真实情况为非混杂因素的变量进行匹配,则会形成过度匹配,造成统计效率下降或引入难以避免的偏倚或增加工作量等;若将真实情况为混杂因素的变量遗漏不予匹配,则会造成混杂偏倚。有向无环图是一种直观的展示不同流行病学研究设计、变量间复杂因果关系的可视化图形语言。本文从有向无环图视角分析匹配在不同观察性研究设计中的作用、匹配性病例对照研究中欲匹配变量的选择标准制定,为今后流行病学研究设计提供一定的参考建议。     

倾向评分配比法及其在Stata软件实现

<正>倾向评分法是由Rosenbaum和Rubin在1983年首次提出~([1]),主要用于观察性研究组中混杂因素的事后均衡,对非研究混杂因素进行类似随机化的均衡处理。近年来,倾向评分法的应用越来越广泛,尤其在临床~([2]),许多学者利用其来降低混杂偏倚,达到协变量     

“探针变量”：观察性研究中混杂因素识别的工具

观察性研究中往往存在未知或未测量的混杂因素,是流行病学因果关联研究中的重大挑战。本文介绍一种可以应用在观察性研究中的一种对未知/未测量混杂因素进行识别和效应评估的工具——“探针变量”。其主要可以分为暴露探针变量、结局探针变量以及中介探针3种形式,前2种不仅可以对未知/未测量混杂因素进行识别,也可以对其效应量进行估计,从...     

群随机研究中倾向性评分匹配与混合效应模型比较

目的 比较倾向性评分匹配(propensity score matching, PSM)法和混合效应模型法在群随机试验中的统计效能,为同类研究的统计分析方法选择提供指导。方法 通过模拟研究与急性缺血性脑卒中疗效评级数据,比较倾向性评分后拟合单因素条件logistic回归分析模型和直接拟合混合效应模型应用于含有混杂因素的群随机试验数据时的统计学性能,说明方法应用场景和选择策略。结果 PSM后,各混杂因素组间均衡性明显改善。条件logistic回归分析模型和混合效应模型处理效应估计结果十分接近,但前者的P值更小。实例分析结果显示,匹配后各混杂因素的标准化均数差异(standardized mean difference, SMD)均控制在0.1以内,条件logistic回归分析模型识别出两个研究结局的处理组间差异;而混合效应模型仅识别出7 d有效率的处理组间差异。结论 PSM可以平衡群随机试验中的混杂因素,提高两组间的可比性,其检验效能较高。推荐在临床群随机试验中优先考虑PSM法,但也要注意其应用条件和局限性。     

基于倾向得分匹配的老年人接种流感疫苗对流感就诊的保护效果研究

目的 评估老年人接种流感疫苗对流感就诊的保护效果,并探讨倾向得分匹配法（PSM）在疫苗保护效果研究中的应用价值。方法 以成都市为研究现场,选取2017年9-12月间接种流感疫苗的60岁及以上老年人作为接种组,采用PSM法选取观察对象作为对照组。匹配后比较两组人群疫苗接种后一年内的流感就诊情况,评价疫苗保护效果。结果 流感疫苗接种组与未接种组各有1442例匹配成功。两组的一般人口学特征、生活方式及健康意识、基本健康状况等特征在匹配前有显著差异,匹配后两组协变量达到均衡。匹配后接种组和对照组流感就诊率分别为0.69%、2.84%,疫苗接种可减少之后一年内75.70%的流感就诊。结论 PSM可有效降低观察性研究组间的混杂偏倚,在疫苗接种效果评价中具有长远的应用价值。流感疫苗接种对降低≥60岁老年人流感就诊具有良好效果,今后有必要采取措施提高流感疫苗接种率。     

倾向评分配比法

配比（matching）,或称匹配,是指选择某些特征上与处理组一致的对照,排除这些因素的混杂作用,从而凸显出研究因素的效应。配比是控制混杂偏倚的常用方法。配比又分为频数配比和个体配比。频数配比（frequency matching）又称为成组配比,是指在选择对照时要求对照组某些重要混杂因素的分布与处理组总体一致。     

倾向评分法中缺失数据的处理

在医学科研中,由于失访、无应答或记录不清等各种原因,经常会遇到数据缺失的问题.某些观察性研究数据,可能会有相当数量的观察对象至少有一个协变量数据缺失.在利用倾向评分法进行数据分析时,由于倾向评分(propensity score,PS)的计算依赖于这些协变量,每个观察单位只要有一个协变量的观察值缺失,就无法估计其PS值.如果忽视这些缺失值的存在,则会导致大量的观察对象在分析过程中被剔除.因此,为了充分利用所收集到的资料,尽可能得到可靠的结果,在计算PS值之前,就有必要识别各协变量的缺失程度和缺失类型,在此基础上对缺失数据进行合理的处理.     

无序多分组数据的倾向性评分匹配算法设计及R程序实现

目的基于倾向性评分匹配(propensity score matching, PSM)的思想设计对无序多分类资料匹配的算法,并开发R语言程序。方法首先运用logistic回归分别计算多个分组水平两两比较的回归系数。用回归系数计算两两组合的组别之间的倾向评分。采用分步流程,先对两组样本量最少的样本进行匹配,之后再用已配成的数据对,在第三组中寻找倾向性评分距离最近的样本。基于以上逻辑过程设计无序三分类资料PSM的算法并编写R语言程序代码,在实际的临床研究数据集上进行测试验证,对匹配前后的数据进行协变量分布不平衡性分析。结果所开发的R程序可顺利运行,能提供稳定可靠的无序三分类资料PSM。在实例数据上,匹配前后协变量的分布不均衡性得到明显降低。结论本研究开发的匹配模算法能有效应用于无序三分组间混杂因素控制。     

The impact of selection bias on vaccine effectiveness estimates from test-negative studies

IntroductionEstimates of vaccine effectiveness (VE) from test-negative studies may be subject to selection bias. In the context of influenza VE, we used simulations to identify situations in which meaningful selection bias can occur. We also analyzed observational study data for evidence of selection bias.MethodsFor the simulation study, we defined a hypothetical population whose members are at risk for acute respiratory illness (ARI) due to influenza and other pathogens. An unmeasured “healthcare seeking proclivity” affects both probability of vaccination and probability of seeking care for an ARI. We varied the direction and magnitude of these effects and identified situations where meaningful bias occurred. For the observational study, we reanalyzed data from the United States Influenza VE Network, an ongoing test-negative study. We compared “bias-naïve” VE estimates to bias-adjusted estimates, which used data from the source populations to correct for sampling bias.ResultsIn the simulation study, an unmeasured care-seeking proclivity could create selection bias if persons with influenza ARI were more (or less) likely to seek care than persons with non-influenza ARI. However, selection bias was only meaningful when rates of care seeking between influenza ARI and non-influenza ARI were very different. In the observational study, the bias-naïve VE estimate of 55% (95% CI, 47-–62%) was trivially different from the bias-adjusted VE estimate of 57% (95% CI, 49-–63%).ConclusionsIn combination, these studies suggest that while selection bias is possible in test-negative VE studies, this bias in unlikely to be meaningful under conditions likely to be encountered in practice. Researchers and public health officials can continue to rely on VE estimates from test-negative studies.     

基于倾向性评分匹配的髋关节术后肺炎危险因素

目的 探讨髋关节手术患者术后肺炎(POP)的危险因素,评价倾向性评分匹配(PSM)在回顾性资料处理中的应用价值.方法 收集某院骨关节科自2018年1月1日—2019年12月31日共1141例髋关节手术患者的资料,应用倾向性评分(PS)对髋关节术后患者资料进行分析.结果 1141例髋关节手术患者出现POP共102例,发生...     

有向无环图在混杂因素识别与控制中的应用及实例分析

观察性研究是流行病学病因研究常用的研究设计,但应用观察性研究进行因果推断时,常由于未经识别、校正的混杂因素的存在,歪曲暴露因素与研究结局之间的真实因果关系。传统混杂因素判断标准在实际应用中不够直观,且有一定局限性,有时甚至出现混杂因素的误判。有向无环图(DAGs)可以直观识别观察性研究中存在的混杂因素,将复杂的因果关系可视化,判断研究中需要校正的最小校正子集,并可避免传统混杂因素判断标准的局限性,结合DAGs还可以指导混杂因素校正方法的选择,在观察性研究中因果推断具有重要指导价值,DAGs在未来的流行病学研究中将有更多的应用。     

A comparison of mixed-effects quantile stratification propensity adjustment strategies for longitudinal treatment effectiveness analyses of continuous outcomes

The propensity adjustment is used to reduce bias in treatment effectiveness estimates from observational data. We show here that a mixed-effects implementation of the propensity adjustment can reduce bias in longitudinal studies of non-equivalent comparison groups. The strategy examined here involves two stages. Initially, a mixed-effects ordinal logistic regression model of propensity for treatment intensity includes variables that differentiate subjects who receive various doses of time-varying treatments. Second, a mixed-effects linear regression model compares the effectiveness of those ordinal doses on a continuous outcome over time. Here, a simulation study compares bias reduction that is achieved by implementing this propensity adjustment through various forms of stratification. The simulations demonstrate that bias decreased monotonically as the number of quantiles used for stratification increased from two to five. This was particularly pronounced with stronger effects of the confounding variables. The quartile and quintile strategies typically removed in excess of 80-90 per cent of the bias detected in unadjusted models; whereas a median-split approach removed from 20 to 45 per cent of bias. The approach is illustrated in an evaluation of the effectiveness of somatic treatments for major depression in a longitudinal, observational study of affective disorders.     

Methodological aspects in the assessment of treatment effects in observational health outcomes studies

Prospective observational studies, which provide information on the effectiveness of interventions in natural settings, may complement results from randomised clinical trials in the evaluation of health technologies. However, observational studies are subject to a number of potential methodological weaknesses, mainly selection and observer bias. This paper reviews and applies various methods to control for selection bias in the estimation of treatment effects and proposes novel ways to assess the presence of observer bias. We also address the issues of estimation and inference in a multilevel setting. We describe and compare the use of regression methods, propensity score matching, fixed-effects models incorporating investigator characteristics, and a multilevel, hierarchical model using Bayesian estimation techniques in the control of selection bias. We also propose to assess the existence of observer bias in observational studies by comparing patient- and investigator-reported outcomes. To illustrate these methods, we have used data from the SOHO (Schizophrenia Outpatient Health Outcomes) study, a large, prospective, observational study of health outcomes associated with the treatment of schizophrenia. The methods used to adjust for differences between treatment groups that could cause selection bias yielded comparable results, reinforcing the validity of the findings. Also, the assessment of observer bias did not show that it existed in the SOHO study. Observational studies, when properly conducted and when using adequate statistical methods, can provide valid information on the evaluation of health technologies.     

Evaluating medication effects outside of clinical trials: new-user designs

Recent clinical trials demonstrating that hormone replacement therapy (HRT) does not prevent coronary heart disease in women have again raised doubts concerning observational studies. Although much of the explanation probably lies in what might be called the "healthy HRT user" effect, another contributing factor may be that most observational studies included many prevalent users: women taking HRT for some time before study follow-up began. This practice can cause two types of bias, both of which plausibly may have contributed to the discrepancy between observational and randomized studies. First, prevalent users are "survivors" of the early period of pharmacotherapy, which can introduce substantial bias if risk varies with time, just as in studies of operative procedures that enroll patients after they have survived surgery. This article provides several examples of medications for which the hazard function varies with time and thus would be subject to prevalent user bias. Second, covariates for drug users at study entry often are plausibly affected by the drug itself. Investigators often do not adjust for these factors on the causal pathway, which may introduce confounding. A new-user design eliminates these biases by restricting the analysis to persons under observation at the start of the current course of treatment. This article thus argues that such designs should be used more frequently in pharmacoepidemiology.     

Understanding and minimizing epidemiologic bias in public health research

Awareness of potential biases is important for both researchers and policy-makers in public health: for researchers when designing and conducting studies, and for policy-makers when reading study reports and making decisions. This paper explains the meaning and importance of epidemiologic bias in public health and discusses how it arises and what can be done to minimize it. Examples of counting participants in a meeting, to which many policy-makers can relate, are used throughout the paper to illustrate bias in general, random error and systematic error, the effect of sample size, the three main categories of bias (selection, information and confounding), stratification and mathematical modeling.     

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.     

A Bayesian approach for correcting exposure misclassification in meta-analysis

In observational studies, misclassification of exposure is ubiquitous and can substantially bias the estimated association between an outcome and an exposure. Although misclassification in a single observational study has been well studied, few papers have considered it in a meta-analysis. Meta-analyses of observational studies provide important evidence for health policy decisions, especially when large randomized controlled trials are unethical or unavailable. It is imperative to account properly for misclassification in a meta-analysis to obtain valid point and interval estimates. In this paper, we propose a novel Bayesian approach to filling this methodological gap. We simultaneously synthesize two (or more) meta-analyses, with one on the association between a misclassified exposure and an outcome (main studies), and the other on the association between the misclassified exposure and the true exposure (validation studies). We extend the current scope for using external validation data by relaxing the “transportability” assumption by means of random effects models. Our model accounts for heterogeneity between studies and can be extended to allow different studies to have different exposure measurements. The proposed model is evaluated through simulations and illustrated using real data from a meta-analysis of the effect of cigarette smoking on diabetic peripheral neuropathy.     

A dynamic adaptation of the propensity score adjustment for effectiveness analyses of ordinal doses of treatment

The propensity score adjustment is a method to reduce bias in observational studies. We propose a strategy that involves a novel combination of three data analytic techniques, which adapts the propensity adjustment for additional perturbations of longitudinal, observational studies. First, ordinal logistic regression examines propensity for ordinal doses of treatment. Second, a mixed-model approach incorporates the multiple treatment trials and multiple episodes that are characteristic of chronically ill subjects. Finally, a mixed-effects grouped-time survival model incorporates the propensity score in treatment effectiveness analyses. The strategy that is applied here to an observational study of affective illness can also be used to evaluate the effectiveness of treatments for other chronic illnesses.