The use of the propensity score for estimating treatment effects: administrative versus clinical data

There is an increasing interest in using administrative data to estimate the treatment effects of interventions. While administrative data are relatively inexpensive to obtain and provide population coverage, they are frequently characterized by lack of clinical detail, often leading to problematic confounding when they are used to conduct observational research. Propensity score methods are increasingly being used to address confounding in estimating the effects of interventions in such studies. Using data on patients discharged from hospital for whom both administrative data and detailed clinical data obtained from chart reviews were available, we examined the degree to which stratifying on the quintiles of propensity scores derived from administrative data was able to balance patient characteristics measured in clinical data. We also determined the extent to which measures of treatment effect obtained using propensity score methods were similar to those obtained using traditional regression methods. As a test case, we examined the treatment effects of ASA and beta-blockers following acute myocardial infarction. We demonstrated that propensity scores developed using administrative data do not necessarily balance patient characteristics contained in clinical data. Furthermore, measures of treatment effectiveness were attenuated when obtained using clinical data compared to when administrative data were used.     

Costs and quality of hospitals in different health care systems: a multi‐level approach with propensity score matching

Cross‐country comparisons of costs and quality between hospitals are often made at the macro level. The goal of this study was to explore methods to compare micro‐level data from hospitals in different health care systems. To do so, we developed a multi‐level framework in combination with a propensity score matching technique using similarly structured data for patients receiving treatment for acute myocardial infarction in German and US Veterans Health Administration hospitals. Our case study shows important differences in results between multi‐level regressions based on matched and unmatched samples. We conclude that propensity score matching techniques are an appropriate way to deal with the usual baseline imbalances across the samples from different countries. Multi‐level models are recommendable to consider the clustered structure of the data when patient‐level data from different hospitals and health care systems are compared. The results provide an important justification for exploring new ways in performing health system comparisons. Copyright © 2010 John Wiley & Sons, Ltd.     

A comparison of 12 algorithms for matching on the propensity score

Propensity‐score matching is increasingly being used to reduce the confounding that can occur in observational studies examining the effects of treatments or interventions on outcomes. We used Monte Carlo simulations to examine the following algorithms for forming matched pairs of treated and untreated subjects: optimal matching, greedy nearest neighbor matching without replacement, and greedy nearest neighbor matching without replacement within specified caliper widths. For each of the latter two algorithms, we examined four different sub‐algorithms defined by the order in which treated subjects were selected for matching to an untreated subject: lowest to highest propensity score, highest to lowest propensity score, best match first, and random order. We also examined matching with replacement. We found that (i) nearest neighbor matching induced the same balance in baseline covariates as did optimal matching; (ii) when at least some of the covariates were continuous, caliper matching tended to induce balance on baseline covariates that was at least as good as the other algorithms; (iii) caliper matching tended to result in estimates of treatment effect with less bias compared with optimal and nearest neighbor matching; (iv) optimal and nearest neighbor matching resulted in estimates of treatment effect with negligibly less variability than did caliper matching; (v) caliper matching had amongst the best performance when assessed using mean squared error; (vi) the order in which treated subjects were selected for matching had at most a modest effect on estimation; and (vii) matching with replacement did not have superior performance compared with caliper matching without replacement. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.     

The performance of different propensity score methods for estimating marginal odds ratios

The propensity score which is the probability of exposure to a specific treatment conditional on observed variables. Conditioning on the propensity score results in unbiased estimation of the expected difference in observed responses to two treatments. In the medical literature, propensity score methods are frequently used for estimating odds ratios. The performance of propensity score methods for estimating marginal odds ratios has not been studied. We performed a series of Monte Carlo simulations to assess the performance of propensity score matching, stratifying on the propensity score, and covariate adjustment using the propensity score to estimate marginal odds ratios. We assessed bias, precision, and mean-squared error (MSE) of the propensity score estimators, in addition to the proportion of bias eliminated due to conditioning on the propensity score. When the true marginal odds ratio was one, then matching on the propensity score and covariate adjustment using the propensity score resulted in unbiased estimation of the true treatment effect, whereas stratification on the propensity score resulted in minor bias in estimating the true marginal odds ratio. When the true marginal odds ratio ranged from 2 to 10, then matching on the propensity score resulted in the least bias, with a relative biases ranging from 2.3 to 13.3 per cent. Stratifying on the propensity score resulted in moderate bias, with relative biases ranging from 15.8 to 59.2 per cent. For both methods, relative bias was proportional to the true odds ratio. Finally, matching on the propensity score tended to result in estimators with the lowest MSE.     

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.     

基于倾向指数匹配法的肝癌患者生存分析

  目的  基于倾向性评分逆概率加权法（IPTW）评价手术、放疗和联合治疗3种方式治疗胃癌患者的疗效, 为胃癌的治疗提供参考依据。  方法  收集2004年1月 — 2013年12月美国国家癌症研究所监测、流行病学与预后项目（SEER）数据库中经胃镜病理诊断确诊的7 005例胃癌患者数据,其中接受手术治疗者3 983例、接受放疗者795例、接受联合治疗者2 227例;采用倾向性评分IPTW法以生存时间和结局为效应指标,分析不同的治疗方法对胃癌患者生存率的影响。  结果  手术组、放疗组和联合治疗组胃癌患者倾向性评分IPTW法加权前中位生存期分别为30、9和38个月,加权后分别为25、11和38个月,加权前、后3组胃癌患者生存曲线间差异均有统计学意义（均P < 0.001）,联合治疗组患者加权前、后的预后均优于手术组和放疗组患者;加权后大部分基线特征绝对标准化平均差异（ASMD）减小,且均 < 0.2,3组患者均衡效果较好;在控制了性别、年龄、种族、婚姻状况、肿瘤大小、病理分化、肿瘤分期、T分期、N分期和远处转移情况等混杂因素后,多因素Cox回归分析结果显示,与手术组胃癌患者相比,放疗组胃癌患者的预后较差（HR = 2.044,95 % CI = 1.770～2.361）,联合治疗组胃癌患者的预后较好（HR = 0.630,95 % CI = 0.573～0.694）。  结论  联合治疗方法疗效优于手术和放疗治疗方法。     

The performance of different propensity score methods for estimating marginal hazard ratios

Propensity score methods are increasingly being used to reduce or minimize the effects of confounding when estimating the effects of treatments, exposures, or interventions when using observational or non‐randomized data. Under the assumption of no unmeasured confounders, previous research has shown that propensity score methods allow for unbiased estimation of linear treatment effects (e.g., differences in means or proportions). However, in biomedical research, time‐to‐event outcomes occur frequently. There is a paucity of research into the performance of different propensity score methods for estimating the effect of treatment on time‐to‐event outcomes. Furthermore, propensity score methods allow for the estimation of marginal or population‐average treatment effects. We conducted an extensive series of Monte Carlo simulations to examine the performance of propensity score matching (1:1 greedy nearest‐neighbor matching within propensity score calipers), stratification on the propensity score, inverse probability of treatment weighting (IPTW) using the propensity score, and covariate adjustment using the propensity score to estimate marginal hazard ratios. We found that both propensity score matching and IPTW using the propensity score allow for the estimation of marginal hazard ratios with minimal bias. Of these two approaches, IPTW using the propensity score resulted in estimates with lower mean squared error when estimating the effect of treatment in the treated. Stratification on the propensity score and covariate adjustment using the propensity score result in biased estimation of both marginal and conditional hazard ratios. Applied researchers are encouraged to use propensity score matching and IPTW using the propensity score when estimating the relative effect of treatment on time‐to‐event outcomes. Copyright © 2012 John Wiley & Sons, Ltd.     

倾向指数方法的蒙特卡罗研究

目的评价由倾向指数方法得到的暴露效果的估计量和统计性质,并探讨其实用性。方法利用计算机模拟对倾向指数方法在无模型误定和有模型误定情况下的偏度和精度进行分析,并与基于模型方法的模拟结果进行比较。结果当存在模型误定时,倾向指数方法比基于模型的方法具有较好的稳健性。结论对于大量、关系复杂的数据,应用倾向指数方法具有较大的灵活性。     

A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study

The propensity score--the probability of exposure to a specific treatment conditional on observed variables--is increasingly being used in observational studies. Creating strata in which subjects are matched on the propensity score allows one to balance measured variables between treated and untreated subjects. There is an ongoing controversy in the literature as to which variables to include in the propensity score model. Some advocate including those variables that predict treatment assignment, while others suggest including all variables potentially related to the outcome, and still others advocate including only variables that are associated with both treatment and outcome. We provide a case study of the association between drug exposure and mortality to show that including a variable that is related to treatment, but not outcome, does not improve balance and reduces the number of matched pairs available for analysis. In order to investigate this issue more comprehensively, we conducted a series of Monte Carlo simulations of the performance of propensity score models that contained variables related to treatment allocation, or variables that were confounders for the treatment-outcome pair, or variables related to outcome or all variables related to either outcome or treatment or neither. We compared the use of these different propensity scores models in matching and stratification in terms of the extent to which they balanced variables. We demonstrated that all propensity scores models balanced measured confounders between treated and untreated subjects in a propensity-score matched sample. However, including only the true confounders or the variables predictive of the outcome in the propensity score model resulted in a substantially larger number of matched pairs than did using the treatment-allocation model. Stratifying on the quintiles of any propensity score model resulted in residual imbalance between treated and untreated subjects in the upper and lower quintiles. Greater balance between treated and untreated subjects was obtained after matching on the propensity score than after stratifying on the quintiles of the propensity score. When a confounding variable was omitted from any of the propensity score models, then matching or stratifying on the propensity score resulted in residual imbalance in prognostically important variables between treated and untreated subjects. We considered four propensity score models for estimating treatment effects: the model that included only true confounders; the model that included all variables associated with the outcome; the model that included all measured variables; and the model that included all variables associated with treatment selection. Reduction in bias when estimating a null treatment effect was equivalent for all four propensity score models when propensity score matching was used. Reduction in bias was marginally greater for the first two propensity score models than for the last two propensity score models when stratification on the quintiles of the propensity score model was employed. Furthermore, omitting a confounding variable from the propensity score model resulted in biased estimation of the treatment effect. Finally, the mean squared error for estimating a null treatment effect was lower when either of the first two propensity scores was used compared to when either of the last two propensity score models was used.     

Comparing the performance of propensity score methods in healthcare database studies with rare outcomes

Nonrandomized studies of treatments from electronic healthcare databases are critical for producing the evidence necessary to making informed treatment decisions, but often rely on comparing rates of events observed in a small number of patients. In addition, studies constructed from electronic healthcare databases, for example, administrative claims data, often adjust for many, possibly hundreds, of potential confounders. Despite the importance of maximizing efficiency when there are many confounders and few observed outcome events, there has been relatively little research on the relative performance of different propensity score methods in this context. In this paper, we compare a wide variety of propensity‐based estimators of the marginal relative risk. In contrast to prior research that has focused on specific statistical methods in isolation of other analytic choices, we instead consider a method to be defined by the complete multistep process from propensity score modeling to final treatment effect estimation. Propensity score model estimation methods considered include ordinary logistic regression, Bayesian logistic regression, lasso, and boosted regression trees. Methods for utilizing the propensity score include pair matching, full matching, decile strata, fine strata, regression adjustment using one or two nonlinear splines, inverse propensity weighting, and matching weights. We evaluate methods via a ‘plasmode’ simulation study, which creates simulated datasets on the basis of a real cohort study of two treatments constructed from administrative claims data. Our results suggest that regression adjustment and matching weights, regardless of the propensity score model estimation method, provide lower bias and mean squared error in the context of rare binary outcomes. Copyright © 2017 John Wiley & Sons, Ltd.     

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.     

基于倾向性评分匹配方法的孕前使用避孕药和妊娠天数的相关性分析

目的 探讨孕前使用避孕药对妊娠天数的影响。 方法 采用倾向性匹配评分方法,以基础资料为匹配条件,将使用避孕药者设为观察组,按照1∶4匹配未使用者为对照组。随访获取妊娠天数资料,进而探讨孕前使用避孕药和妊娠天数的关系。 结果 共匹配避孕药组241例,对照组964例,匹配以后各项资料均未见显著性差异(P＞0.05)。Cox回归分析发现,在调整基础资料后,使用避孕药可使妊娠天数缩短,危害比(hazard ratio,HR)为0.85(95%CI:0.74~0.98)。亚组分析发现,年龄≥31岁、体质指数≥24、无妊娠史三个亚组,孕前服用避孕药使妊娠天数缩短,HR(95%CI)分别为0.76(0.59,0.97)、0.67 (0.46,0.98)、0.78(0.63,0.96)。 结论 孕前服用避孕药可能缩短妊娠天数,特别是高龄、肥胖、初次妊娠女性,更应注意避孕药的使用。     

基于倾向性评分匹配法的血浆D-二聚体升高与新型冠状病毒肺炎重症化相关性研究

目的　评估血浆D-二聚体（D-dimer, D-D）与新型冠状病毒肺炎（coronavirus disease 2019, COVID-19）重症化的相关性。方法　回顾性分析2020年1月20日—2月8日在阜阳市第二人民医院住院的102例COVID-19患者的临床资料,将患者按血浆D-D是否升高分为D-D不升高组与D-D持续升高组,比较2组患者的临床资料,并进行倾向性评分匹配及生存分析,评价D-D水平对于COVID-19重症化的作用。结果　最终纳入102例COVID-19确诊患者,中位年龄42.2（31.7,49.6）岁,60例（58.8%）为男性,19例（18.6%）存在基础疾病,6例（5.9%）有吸烟史,7例（6.9%）有间断饮酒史,74例（72.5%）有流行病学史。与D-D不升高组（n=85）相比,D-D持续升高组（n=17）患者的年龄更高,其中年龄＞50岁的患者占58.8%,淋巴细胞绝对值更低,LDH水平更高（P均＜0.05）。D-D持续升高组有8例患者出现了重症化（47.1%）,D-D不升高组有7例（8.2%）出现重症化（P＜0.05）。基于多因素Logistic回归进行匹配,匹配成功后D-D升高组有8例（47.1%）患者出现了重症化,D-D不升高组有4例（11.8%）出现重症化（P＜0.05）,CoX风险比例回归分析结果显示D-D持续升高是疾病进展的独立风险因素,其HR为4.372（95% CI: 1.313～14.556）。结论　血浆D-D的持续升高是COVID-19重症化的高危因素。     

The use of propensity score methods with survival or time‐to‐event outcomes: reporting measures of effect similar to those used in randomized experiments

Propensity score methods are increasingly being used to estimate causal treatment effects in observational studies. In medical and epidemiological studies, outcomes are frequently time‐to‐event in nature. Propensity‐score methods are often applied incorrectly when estimating the effect of treatment on time‐to‐event outcomes. This article describes how two different propensity score methods (matching and inverse probability of treatment weighting) can be used to estimate the measures of effect that are frequently reported in randomized controlled trials: (i) marginal survival curves, which describe survival in the population if all subjects were treated or if all subjects were untreated; and (ii) marginal hazard ratios. The use of these propensity score methods allows one to replicate the measures of effect that are commonly reported in randomized controlled trials with time‐to‐event outcomes: both absolute and relative reductions in the probability of an event occurring can be determined. We also provide guidance on variable selection for the propensity score model, highlight methods for assessing the balance of baseline covariates between treated and untreated subjects, and describe the implementation of a sensitivity analysis to assess the effect of unmeasured confounding variables on the estimated treatment effect when outcomes are time‐to‐event in nature. The methods in the paper are illustrated by estimating the effect of discharge statin prescribing on the risk of death in a sample of patients hospitalized with acute myocardial infarction. In this tutorial article, we describe and illustrate all the steps necessary to conduct a comprehensive analysis of the effect of treatment on time‐to‐event outcomes. © 2013 The authors. Statistics in Medicine published by 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.     

The use of quantile regression in health care research: a case study examining gender differences in the timeliness of thrombolytic therapy

Investigators are frequently interested in determining patient and system characteristics associated with delays in the provision of essential medical treatment. Investigators have typically used either multiple linear regression or Cox proportional hazards models to assess the impact of patient and system characteristics on the timeliness of medical treatment. A drawback to the use of these two methods is that they allow, at best, a partial exploration of how a distribution of delays in treatment or of waiting times changes with patient characteristics. In contrast, quantile regression models allow one to assess how any quantile of a conditional distribution changes with patient characteristics. We illustrate the utility of quantile regression by examining gender differences in the delivery of thrombolysis in patients with an acute myocardial infarction. We demonstrate that richer inferences can be drawn through the use of quantile regression. Females were more likely to experience delays in treatment compared to males. Furthermore, gender had a greater impact upon those patients who had the greatest delays in treatment. Investigators who want to determine how a distribution of delays in treatment or of waiting times changes with patient or system characteristics should consider complementing their analyses with the use of quantile regression.     

A propensity score approach to estimating the cost-effectiveness of medical therapies from observational data

Health summary measures are commonly used by policy makers to help make decisions on the allocation of societal resources for competing medical treatments. The net monetary benefit is a health summary measure that overcomes the statistical limitations of a popular measure namely, the cost-effectiveness ratio. We introduce a linear model framework to estimate propensity score adjusted net monetary benefit. This method provides less biased estimates in the presence of significant differences in baseline measures and demographic characteristics between treatment groups in quasi-randomized or observational studies. Simulation studies were conducted to better understand the utility of propensity score adjusted estimates of net monetary benefits when important covariates are unobserved. The results indicated that the propensity score adjusted net monetary benefit provides a robust measure of cost-effectiveness in the presence of hidden bias. The methods are illustrated using data from SEER-Medicare for the treatment of bladder cancer.     

The use of bootstrapping when using propensity‐score matching without replacement: a simulation study

Propensity‐score matching is frequently used to estimate the effect of treatments, exposures, and interventions when using observational data. An important issue when using propensity‐score matching is how to estimate the standard error of the estimated treatment effect. Accurate variance estimation permits construction of confidence intervals that have the advertised coverage rates and tests of statistical significance that have the correct type I error rates. There is disagreement in the literature as to how standard errors should be estimated. The bootstrap is a commonly used resampling method that permits estimation of the sampling variability of estimated parameters. Bootstrap methods are rarely used in conjunction with propensity‐score matching. We propose two different bootstrap methods for use when using propensity‐score matching without replacementand examined their performance with a series of Monte Carlo simulations. The first method involved drawing bootstrap samples from the matched pairs in the propensity‐score‐matched sample. The second method involved drawing bootstrap samples from the original sample and estimating the propensity score separately in each bootstrap sample and creating a matched sample within each of these bootstrap samples. The former approach was found to result in estimates of the standard error that were closer to the empirical standard deviation of the sampling distribution of estimated effects. © 2014 The Authors. Statistics in Medicine Published by John Wiley & Sons, Ltd.     

The effect of a constraint on the maximum number of controls matched to each treated subject on the performance of full matching on the propensity score when estimating risk differences

Many observational studies estimate causal effects using methods based on matching on the propensity score. Full matching on the propensity score is an effective and flexible method for utilizing all available data and for creating well‐balanced treatment and control groups. An important component of the full matching algorithm is the decision about whether to impose a restriction on the maximum ratio of controls matched to each treated subject. Despite the possible effect of this restriction on subsequent inferences, this issue has not been examined. We used a series of Monte Carlo simulations to evaluate the effect of imposing a restriction on the maximum ratio of controls matched to each treated subject when estimating risk differences. We considered full matching both with and without a caliper restriction. When using full matching with a caliper restriction, the imposition of a subsequent constraint on the maximum ratio of the number of controls matched to each treated subject had no effect on the quality of inferences. However, when using full matching without a caliper restriction, the imposition of a constraint on the maximum ratio of the number of controls matched to each treated subject tended to result in an increase in bias in the estimated risk difference. However, this increase in bias tended to be accompanied by a corresponding decrease in the sampling variability of the estimated risk difference. We illustrate the consequences of these restrictions using observational data to estimate the effect of medication prescribing on survival following hospitalization for a heart attack.     

倾向指数匹配法在医院感染经济负担评价中的应用

目的应用倾向指数匹配法(PSM)提高各协变量的均衡性,为医院感染经济负担评价提供新方法。方法选取某大型综合医院2015年1月1日—2016年12月31日出院的158 976例患者为研究对象,使用SPSS22.0软件对医院感染患者和非医院感染患者进行1∶1的PSM匹配,采用匹配后的均衡数据库分析医院感染对患者住院天数和住院费用的影响。结果经PSM共成功匹配2 388对,匹配成功比率为97.75%,匹配后医院感染组和非医院感染组组间协变量各组比较,差异均无统计学意义(均P0.05)。医院感染组和非医院感染组患者住院时间中位数分别为21、10 d,中位数差值为11 d,两组比较差异有统计学意义(P0.05)。医院感染组和非医院感染组患者住院总费用中位数分别为33 618.38、15 004.93元,中位数差值为18 613.45元,两组比较差异有统计学意义(P0.05)。除其他费用无统计学差异外,医院感染组床位费、西药费、中药费、治疗费、检查费、化验费、诊查费、护理费、输血费、手术费、材料费、麻醉费均高于非医院感染组(均P0.05),其中两组间西药费差值最大,为6 987.00元。结论 PSM结果显示医院感染造成了严重的经济负担,PSM可以为医院感染经济负担评价提供新方法。