不同混杂结构条件下各倾向性评分方法的模拟比较研究

目的通过构建不同混杂结构的处理因素模型和结局模型、不同相关性的协变量,比较多种倾向性评分方法在结局模型为线性回归模型的情况下估计处理效应的优劣。方法采用Monte Carlo模拟方法,通过构建四种由简单到复杂的不同结构的混杂模型,生成相应的数据集,再分别应用倾向性评分匹配、回归调整、加权以及分层的方法估计处理效应并进行比较。评价指标包括点估计、标准误、相对偏倚、均方误差。结果在结局模型为线性回归模型情况下,倾向性评分回归调整法估计的相对偏倚最小,稳定性也最好。匹配法卡钳值取0.02较卡钳值取倾向性评分标准差的0.2倍估计的相对偏倚更小。当处理因素模型中含有非线性效应时,用逆概率加权法估计的偏倚较大,并且加权法估计的标准误也最大。倾向性评分分层法在各种情况下估计的相对偏倚都较大。结论倾向性评分回归调整法能够较好地估计处理效应,并且在各种情况下估计都较为稳健。建议当协变量与处理因素和结局变量的关系无法确定时,这四种方法中可以考虑优先使用回归调整法。     

不同混杂结构下广义倾向性评分法的模拟研究及应用

目的 通过构建存在不同混杂结构的广义倾向性评分(generalized propensity score,GPS)模型和结局模型,探索比较三种GPS估计法:广义倾向性评分-最小二乘法(generalized propensity score-ordinary least squares,GPS-OLS),广义倾向性评分...     

疾病风险评分在药物流行病学研究中的应用

疾病风险评分通过平衡不同组间研究对象的基线疾病风险以控制高维数据结构中的混杂效应,从而减小暴露因素效应估计的偏倚,因此在利用医疗数据库探索药物疗效或不良反应等规律的药物流行病学研究中有重要的应用价值。尽管疾病风险评分方法在很多情况下具有与倾向性评分相似的作用,而且在一些特殊暴露条件的研究中具有倾向性评分与传统混杂控制方法不可比拟的优势,但目前疾病风险评分在药物流行病学研究中应用范围远不及倾向性评分广泛。基于对疾病风险评分方法在药物流行病学研究中应用价值的考量,本文阐述了疾病风险评分的原理、模型构建、评分估计和应用的方法,以期为疾病风险评分方法在药物流行病学研究中的应用提供参考。     

观察性研究中三种控制混杂偏倚的匹配方法比较

目的 探讨观察性研究中用于混杂偏倚控制的倾向性评分匹配、马氏距离匹配和遗传匹配三种方法的性能。方法 针对连续型结局变量,设定混杂变量与处理分组变量之间具有不同复杂度的回归模型结构,采用Monte-Carlo模拟方法比较三种匹配方法在处理组间效应估计和匹配前后自变量均衡的区别,进而对三种方法性能进行评估。结果 在给定的模拟情形下,相比于倾向性评分匹配和马氏距离匹配,遗传匹配法得出的效应估计偏差最小,匹配后两组自变量均衡性最好。结论 遗传匹配在三种匹配方法中表现出较好的统计性能,可考虑作为观察性研究中控制混杂偏倚优先推荐的匹配方法。     

利用因果森林估计异质性人群下个体的处理效应

目的 探讨因果森林在异质性人群中估计个体处理效应的有效性及如何应用于实例数据以挖掘异质性人群特征。方法 设计4种模拟方案,通过模拟试验验证因果森林在不同处理效应环境设置下对个体处理效应进行估计的效果,并应用于右心导管插入术实例数据集进行分析。结果 模拟试验结果表明,在4种不同效应值设置下,用因果森林方法所估计的个体处理效应值都能与总体效应相吻合,符合预期分布;实例数据分析结果显示绝大多数患者个体处理效应为正值,使用RHC会导致该样本人群180 d死亡率增高,2月生存模型估计概率和白蛋白含量偏低的患者在使用RHC后更倾向于有较低的死亡风险。结论 因果森林能够有效地估计个体处理效应,为个体是否接受某种处理提供建议。     

具有相关关系的二分类资料处理方法比较

目的探讨分析具有相关关系的二分类资料的有效处理方法。方法采用蒙特卡罗模拟比较广义估计方程和广义随机效应模型与一般logistic回归在处理具有相关关系的二分类资料的区别。结果一般logisitc回归处理相关关系的二分类资料时假阳性率增加。广义估计方程与广义随机效应模型是处理该类型资料时,I类错误能稳定控制在0.05左右,且检验效能基本一致。结论广义估计方程和广义随机效应模型是处理具有相关关系的二分类资料的合适方法,不能采用一般logistic回归代替。     

基于倾向性评分匹配及广义线性模型的医院感染经济损失研究

目的同时用倾向性评分匹配及广义线性模型的方法研究医院感染的直接经济损失,为感控措施的制定提供参考。方法回顾性调查中国科学院大学深圳医院(西院区)2016年6月-2018年11月所有出院患者的住院信息,将患者分为医院感染组及非医院感染组,用倾向性评分匹配法和广义线性模型法研究病例组与对照组住院天数和住院费用增量。结果倾向性评分匹配后,医院感染组较非医院感染组住院天数中位数增加14.00 d,2.00倍(Z=-21.485,P<0.001),住院费用中位数增加17 264.00元,1.57倍(Z=-13.576,P<0.001),与广义线性模型法得出的结果相似;神经外科感染延长住院时间最长,达31.00 d(Z=-8.225,P<0.001);而重症监护病房感染增加的住院费用最多,达80 096.00元(Z=-6.371,P<0.001);结论医院感染明显增加患者住院时间和住院费用,神经外科和重症监护病房的医院感染,应作为感控工作的重点制定有针对性的措施加以控制。     

随机对照试验不依从数据分析方法的比较研究

目的比较依从者的平均因果效应(CACE)、意向性分析(ITT)、遵循研究方案分析(PP)和接受干预措施分析(AT),在分析随机对照试验不依从数据的效果,探索各种方法的适用条件,为实际数据分析提供科学依据。方法通过SAS软件模拟产生不依从数据,处理措施的因果效应使用CACE、ITT、PP和AT进行估计,以平均偏倚、均方根误差、标准误和检验效能作为评价指标,比较各种方法的估计效果。结果在各种参数组合下,以平均偏倚、均方根误差和检验效能作为评价指标,CACE的估计效果均优于ITT、PP和AT。依从率低于50%时,CACE估计的标准误低于PP,高于ITT和AT;依从率高于50%时,CACE估计的标准误均低于ITT、PP和AT。结论当满足CACE模型假设时,CACE估计随机对照试验不依从数据因果效应的效果优于三种传统分析方法,能够提供更加稳健、无偏的处理效应估计值。     

广义估计方程与准最小二乘在社区纵向数据分析中的应用

目的 应用广义估计方程和准最小二乘方法分析社区卫生服务中心纵向数据,探讨纵向数据分析的问题,为社区的随访的纵向数据的分析提供科学的方法. 方法 对收集的社区卫生服务中心的糖尿病病人血糖的纵向数据,分别使用广义估计方程和准最小二乘方法以及传统的线性回归模型进行分析并比较结果.同时比较三种方法的标准化残差图. 结果 广义估计方程不收敛时与传统线性模型的结果相同,显示糖尿病人血糖与教育水平相关,而广义估计方程收敛时与准最小二乘的结果相同,显示教育无统计学意义.从标准化残差图看广义估计方程和准最小二乘法对数据的拟合比传统回归好. 结论 广义估计方程和准最小二乘法都能有效的处理纵向数据.与广义估计方程相比,准最小二乘法有一些优势.     

倾向性评分法和马氏距离法在匹配中的比较与应用

目的比较倾向性评分法与马氏距离法在匹配中的效果,在医学数据中验证倾向性评分悖论的观点。方法通过最邻近匹配及卡钳匹配选择最佳匹配方法,计算不同卡钳值下删减个体数后样本的不平衡性,比较倾向性评分法与马氏距离法的稳定性。结果对于本研究的数据,倾向性评分法的卡钳匹配是最佳的匹配方法;倾向性评分法在删减个体数达到一定后,继续删减匹配较差个体会增加样本的不平衡性,马氏距离匹配的样本不平衡性随着删减个体数的增加而减少。结论倾向性评分匹配法调整混杂时,不宜删减较多个体寻找更加精确匹配的匹配集。     

A tutorial on propensity score estimation for multiple treatments using generalized boosted models

The use of propensity scores to control for pretreatment imbalances on observed variables in non‐randomized or observational studies examining the causal effects of treatments or interventions has become widespread over the past decade. For settings with two conditions of interest such as a treatment and a control, inverse probability of treatment weighted estimation with propensity scores estimated via boosted models has been shown in simulation studies to yield causal effect estimates with desirable properties. There are tools (e.g., the twang package in R) and guidance for implementing this method with two treatments. However, there is not such guidance for analyses of three or more treatments. The goals of this paper are twofold: (1) to provide step‐by‐step guidance for researchers who want to implement propensity score weighting for multiple treatments and (2) to propose the use of generalized boosted models (GBM) for estimation of the necessary propensity score weights. We define the causal quantities that may be of interest to studies of multiple treatments and derive weighted estimators of those quantities. We present a detailed plan for using GBM to estimate propensity scores and using those scores to estimate weights and causal effects. We also provide tools for assessing balance and overlap of pretreatment variables among treatment groups in the context of multiple treatments. A case study examining the effects of three treatment programs for adolescent substance abuse demonstrates the methods. Copyright © 2013 John Wiley & Sons, Ltd.     

Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study

Propensity score methods are increasingly being used to estimate causal treatment effects in the medical literature. Conditioning on the propensity score results in unbiased estimation of the expected difference in observed responses to two treatments. The degree to which conditioning on the propensity score introduces bias into the estimation of the conditional odds ratio or conditional hazard ratio, which are frequently used as measures of treatment effect in observational studies, has not been extensively studied. We conducted Monte Carlo simulations to determine the degree to which propensity score matching, stratification on the quintiles of the propensity score, and covariate adjustment using the propensity score result in biased estimation of conditional odds ratios, hazard ratios, and rate ratios. We found that conditioning on the propensity score resulted in biased estimation of the true conditional odds ratio and the true conditional hazard ratio. In all scenarios examined, treatment effects were biased towards the null treatment effect. However, conditioning on the propensity score did not result in biased estimation of the true conditional rate ratio. In contrast, conventional regression methods allowed unbiased estimation of the true conditional treatment effect when all variables associated with the outcome were included in the regression model. The observed bias in propensity score methods is due to the fact that regression models allow one to estimate conditional treatment effects, whereas propensity score methods allow one to estimate marginal treatment effects. In several settings with non-linear treatment effects, marginal and conditional treatment effects do not coincide.     

Generalized propensity score for estimating the average treatment effect of multiple treatments

The propensity score method is widely used in clinical studies to estimate the effect of a treatment with two levels on patient's outcomes. However, due to the complexity of many diseases, an effective treatment often involves multiple components. For example, in the practice of Traditional Chinese Medicine (TCM), an effective treatment may include multiple components, e.g. Chinese herbs, acupuncture, and massage therapy. In clinical trials involving TCM, patients could be randomly assigned to either the treatment or control group, but they or their doctors may make different choices about which treatment component to use. As a result, treatment components are not randomly assigned. Rosenbaum and Rubin proposed the propensity score method for binary treatments, and Imbens extended their work to multiple treatments. These authors defined the generalized propensity score as the conditional probability of receiving a particular level of the treatment given the pre-treatment variables. In the present work, we adopted this approach and developed a statistical methodology based on the generalized propensity score in order to estimate treatment effects in the case of multiple treatments. Two methods were discussed and compared: propensity score regression adjustment and propensity score weighting. We used these methods to assess the relative effectiveness of individual treatments in the multiple-treatment IMPACT clinical trial. The results reveal that both methods perform well when the sample size is moderate or large.     

Addressing missing data in confounders when estimating propensity scores for continuous exposures

Propensity score models are frequently used to estimate causal effects in observational studies. One unresolved issue in fitting these models is handling missing values in the propensity score model covariates. As these models usually contain a large set of covariates, using only individuals with complete data significantly decreases the sample size and statistical power. Several missing data imputation approaches have been proposed, including multiple imputation (MI), MI with missingness pattern (MIMP), and treatment mean imputation. Generalized boosted modeling (GBM), which is a nonparametric approach to estimate propensity scores, can automatically handle missingness in the covariates. Although the performance of MI, MIMP, and treatment mean imputation have previously been compared for binary treatments, they have not been compared for continuous exposures or with single imputation and GBM. We compared these approaches in estimating the generalized propensity score (GPS) for a continuous exposure in both a simulation study and in empirical data. Using GBM with the incomplete data to estimate the GPS did not perform well in the simulation. Missing values should be imputed before estimating propensity scores using GBM or any other approach for estimating the GPS.     

Using Propensity Scores for Causal Inference: Pitfalls and Tips

Methods based on propensity score (PS) have become increasingly popular as a tool for causal inference. A better understanding of the relative advantages and disadvantages of the alternative analytic approaches can contribute to the optimal choice and use of a specific PS method over other methods. In this article, we provide an accessible overview of causal inference from observational data and two major PS-based methods (matching and inverse probability weighting), focusing on the underlying assumptions and decision-making processes. We then discuss common pitfalls and tips for applying the PS methods to empirical research and compare the conventional multivariable outcome regression and the two alternative PS-based methods (ie, matching and inverse probability weighting) and discuss their similarities and differences. Although we note subtle differences in causal identification assumptions, we highlight that the methods are distinct primarily in terms of the statistical modeling assumptions involved and the target population for which exposure effects are being estimated.Key words: propensity score, matching, inverse probability weighting, target population     

Assessing the performance of the generalized propensity score for estimating the effect of quantitative or continuous exposures on binary outcomes

Propensity score methods are increasingly being used to estimate the effects of treatments and exposures when using observational data. The propensity score was initially developed for use with binary exposures. The generalized propensity score (GPS) is an extension of the propensity score for use with quantitative or continuous exposures (eg, dose or quantity of medication, income, or years of education). We used Monte Carlo simulations to examine the performance of different methods of using the GPS to estimate the effect of continuous exposures on binary outcomes. We examined covariate adjustment using the GPS and weighting using weights based on the inverse of the GPS. We examined both the use of ordinary least squares to estimate the propensity function and the use of the covariate balancing propensity score algorithm. The use of methods based on the GPS was compared with the use of G‐computation. All methods resulted in essentially unbiased estimation of the population dose‐response function. However, GPS‐based weighting tended to result in estimates that displayed greater variability and had higher mean squared error when the magnitude of confounding was strong. Of the methods based on the GPS, covariate adjustment using the GPS tended to result in estimates with lower variability and mean squared error when the magnitude of confounding was strong. We illustrate the application of these methods by estimating the effect of average neighborhood income on the probability of death within 1 year of hospitalization for an acute myocardial infarction.     

The use of prognostic scores for causal inference with general treatment regimes

In nonrandomised studies, inferring causal effects requires appropriate methods for addressing confounding bias. Although it is common to adopt propensity score analysis to this purpose, prognostic score analysis has recently been proposed as an alternative strategy. While both approaches were originally introduced to estimate causal effects for binary interventions, the theory of propensity score has since been extended to the case of general treatment regimes. Indeed, many treatments are not assigned in a binary fashion and require a certain extent of dosing. Hence, researchers may often be interested in estimating treatment effects across multiple exposures. To the best of our knowledge, the prognostic score analysis has not been yet generalised to this case. In this article, we describe the theory of prognostic scores for causal inference with general treatment regimes. Our methods can be applied to compare multiple treatments using nonrandomised data, a topic of great relevance in contemporary evaluations of clinical interventions. We propose estimators for the average treatment effects in different populations of interest, the validity of which is assessed through a series of simulations. Finally, we present an illustrative case in which we estimate the effect of the delay to Aspirin administration on a composite outcome of death or dependence at 6 months in stroke patients.     

A novel approach for propensity score matching and stratification for multiple treatments: Application to an electronic health record–derived study

Currently, methods for conducting multiple treatment propensity scoring in the presence of high-dimensional covariate spaces that result from “big data” are lacking—the most prominent method relies on inverse probability treatment weighting (IPTW). However, IPTW only utilizes one element of the generalized propensity score (GPS) vector, which can lead to a loss of information and inadequate covariate balance in the presence of multiple treatments. This limitation motivates the development of a novel propensity score method that uses the entire GPS vector to establish a scalar balancing score that, when adjusted for, achieves covariate balance in the presence of potentially high-dimensional covariates. Specifically, the generalized propensity score cumulative distribution function (GPS-CDF) method is introduced. A one-parameter power function fits the CDF of the GPS vector and a resulting scalar balancing score is used for matching and/or stratification. Simulation results show superior performance of the new method compared to IPTW both in achieving covariate balance and estimating average treatment effects in the presence of multiple treatments. The proposed approach is applied to a study derived from electronic medical records to determine the causal relationship between three different vasopressors and mortality in patients with non-traumatic aneurysmal subarachnoid hemorrhage. Results suggest that the GPS-CDF method performs well when applied to large observational studies with multiple treatments that have large covariate spaces.     

Uncertainty in the design stage of two-stage Bayesian propensity score analysis

The two-stage process of propensity score analysis (PSA) includes a design stage where propensity scores (PSs) are estimated and implemented to approximate a randomized experiment and an analysis stage where treatment effects are estimated conditional on the design. This article considers how uncertainty associated with the design stage impacts estimation of causal effects in the analysis stage. Such design uncertainty can derive from the fact that the PS itself is an estimated quantity, but also from other features of the design stage tied to choice of PS implementation. This article offers a procedure for obtaining the posterior distribution of causal effects after marginalizing over a distribution of design-stage outputs, lending a degree of formality to Bayesian methods for PSA that have gained attention in recent literature. Formulation of a probability distribution for the design-stage output depends on how the PS is implemented in the design stage, and propagation of uncertainty into causal estimates depends on how the treatment effect is estimated in the analysis stage. We explore these differences within a sample of commonly used PS implementations (quantile stratification, nearest-neighbor matching, caliper matching, inverse probability of treatment weighting, and doubly robust estimation) and investigate in a simulation study the impact of statistician choice in PS model and implementation on the degree of between- and within-design variability in the estimated treatment effect. The methods are then deployed in an investigation of the association between levels of fine particulate air pollution and elevated exposure to emissions from coal-fired power plants.     

A comparison of propensity score methods: a case-study estimating the effectiveness of post-AMI statin use

There is an increasing interest in the use of propensity score methods to estimate causal effects in observational studies. However, recent systematic reviews have demonstrated that propensity score methods are inconsistently used and frequently poorly applied in the medical literature. In this study, we compared the following propensity score methods for estimating the reduction in all-cause mortality due to statin therapy for patients hospitalized with acute myocardial infarction: propensity-score matching, stratification using the propensity score, covariate adjustment using the propensity score, and weighting using the propensity score. We used propensity score methods to estimate both adjusted treated effects and the absolute and relative risk reduction in all-cause mortality. We also examined the use of statistical hypothesis testing, standardized differences, box plots, non-parametric density estimates, and quantile-quantile plots to assess residual confounding that remained after stratification or matching on the propensity score. Estimates of the absolute reduction in 3-year mortality ranged from 2.1 to 4.5 per cent, while estimates of the relative risk reduction ranged from 13.3 to 17.0 per cent. Adjusted estimates of the reduction in the odds of 3-year death varied from 15 to 24 per cent across the different propensity score methods.