基于倾向指数匹配法的肝癌患者生存分析

目的 用倾向指数匹配法均衡射频消融术( RFA)和外科切除术(SR)两组中期肝癌病人组间的协变量后,评价两种治疗方式的效果.方法 应用倾向指数卡钳匹配法对两组肝癌病人进行匹配,得到两组间各协变量均衡的样本,并用新样本做生存分析.结果 使用卡钳匹配法对肝癌病人两组间的协变量进行平衡,匹配前年龄、child分级、家族史、是否有乙肝在两组间的不均衡在匹配以后达到了均衡.匹配之前,Cox回归分析表明,Child分级高、有肝癌家族史、HbsAg阳性、过高的AFP及GT等是影响病人死亡率的危险因素(风险比HR＞1,P＜0.05),但是射频消融术和外科切除术两组间死亡率的差异无统计学意义(P =0.202);匹配之后,射频消融术组和外科切除术组的1年生存率分别为33.24％、44.37％,2年生存率分别为12.75％、16.70％,中位生存时间分别为0.66年和0.84年,两组间生存率的差异有统计学意义(P =0.032).结论 外科切除术的治疗效果优于射频消融术的治疗效果.     

倾向得分区间匹配法用于非随机对照试验的探索与研究

目的探索研究倾向得分区间匹配法在非随机对照试验中用于均衡组间混杂因素的能力,并与logistic回归分析方法和倾向得分卡钳匹配进行比较。方法通过Monte Carlo模拟分析倾向得分区间匹配法处理二分类资料的能力,并与传统的logistic回归方法以及倾向得分卡钳匹配法进行比较,通过I类错误、检验效能、标准化差异以及匹配比例等指标进行综合评价。结果倾向得分区间匹配法与logistic回归法以及倾向得分卡钳匹配法的检验效能、I类错误、标准化差异和匹配比例四个评价指标无明显差异。结论在观察性研究和流行病学研究中,采用倾向得分区间匹配法均衡组间协变量得到真实的处理效应具有很高的实用价值。     

潍坊地区中学生血压偏高与体质量指数的关系

分析青少年血压偏高与体质量指数的关系,为控制中学生体重减少血压偏高提供依据.方法 分层整群随机抽取潍坊地区6所初中、高中学校13~18岁学生2 258名,采用倾向指数法均衡混杂因素,计算不同BMI分组间青少年的倾向指数,然后按倾向指数进行组间卡钳匹配,对匹配前后的数据进行t检验与秩和检验等.结果 倾向指数法匹配前,协变量精神紧张、睡眠状况在正常与超重中学生间分布差异有统计学意义(Z值分别为-2.607,-1.989,P值均<0.05);倾向指数匹配后,所有协变量在正常组与超重组间分布差异均无统计学意义(P值均>0.05).倾向指数匹配后BMI正常组与BMI异常(超重/肥胖)组间青少年血压状况分布差异有统计学意义(Z=-2.631,P<0.05).结论 控制青少年的体重可以减少血压偏高的罹患风险;倾向指数法可以有效控制组间偏倚,从而对分析结论做出正确评价.     

倾向指数分层法的模拟研究

目的用SAS程序模拟研究倾向指数分层法处理非随机化试验数据的效果。方法利用蒙特-卡罗模拟法产生有三个协变量的两组随机样本,以分组变量为因变量,以协变量为自变量建立logistic回归模型,并计算研究对象的倾向指数,然后按照倾向指数分成五层,得到一层内各个协变量均衡的处理组与对照组样本。结果协变量间不均衡的两个处理组在分层以后组内得到了均衡。结论倾向指数法是一种有效处理非随机化试验数据的方法,具有重要的应用价值。     

倾向指数匹配法在筛选青少年血压偏高危险因素中的应用

目的探讨倾向指数匹配法在青少年血压偏高危险因素筛选中的应用,分析家族史对青少年血压偏高的影响。方法采用分层整群随机抽样方法,选取潍坊地区青少年536例,利用倾向指数匹配法均衡混杂协变量,并对匹配前后的数据进行相应统计分析。结果协变量超重、失眠、户籍在倾向指数匹配前组间分布不均衡(P0.05),倾向指数匹配以后,所有协变量在组间分布皆达到均衡(P0.05);有家族史组与无家族史组间青少年血压状况分布差异有统计学意义(P=0.0020.05)。结论有家族史青少年罹患高血压风险高于无家族史组青少年,应加强相关人群的健康教育与监控;倾向指数匹配法可以有效控制组间偏倚,在筛选青少年血压偏高危险因素方面效果明显。     

基于倾向指数匹配的潍坊市城乡居民首诊机构选择差异研究

目的以首诊机构选择为视角,探讨城乡基本公共卫生服务可及性效果评价的方法。方法随机抽取潍坊市城乡居民1955例,采用logistic回归模型计算居民城乡分组间的倾向指数,按照倾向指数进行组间卡钳匹配,对匹配前后的数据进行统计学分析。结果经倾向指数匹配后,年龄、文化程度、保险类型等协变量达到了均衡;城乡居民对首诊机构的选择差异无统计学意义(P0.05)。结论潍坊市城乡居民对首诊机构的选择意愿相同,基本公共卫生服务可及性效果明显。     

倾向评分匹配法在横断面资料处理中的应用价值研究

目的探讨心血管疾病与骨关节炎疾病患病情况之间的相关性,进而评价倾向评分匹配法在横断面资料处理中的应用价值。方法对山西省阳城县和偏关县农村社区7126名16岁以上常住居民进行心血管疾病与骨关节炎的相关调查,根据心血管患病情况分为2组进行比较;利用Stata14.0对组间协变量进行倾向评分卡钳匹配,计算2组的倾向评分,对匹配前后心血管疾病组与非心血管疾病组发生骨关节炎的危险性进行评估。结果心血管疾病组与非心血管疾病组各有1123例匹配成功,匹配前2组骨关节炎患病率比较差异有统计学意义(P0.001);经倾向评分匹配后,年龄、性别、职业、口味、BMI、吸烟情况等协变量达到了均衡,2组骨关节炎患病率比较差异仍有统计学意义(P0.001)。结论心血管疾病与骨关节炎患病之间有一定的相关性,但具体机制仍需进一步的研究证实。倾向评分匹配法能有效降低观察性研究组间的混杂偏倚,在横断面资料数据处理中有广阔的应用前景。     

非随机化临床试验中倾向指数的应用

[目的]探讨倾向指数处理非随机化临床试验数据的效果,同时编制出基于倾向指数分层法的SAS程序并加以实现. [方法]运用回归模型计算研究对象的倾向指数,然后按照倾向指数进行分层从而得到一个新的各个协变量均衡的治疗组与对照组样本. [结果]使用五等分分层法对临床试验中的非随机化数据进行处理,分层前两组协变量问的不均衡在分层以后达到了均衡. [结论]在新药临床试验研究中,倾向指数是一种有效的处理非随机化数据的方法.     

基于倾向指数匹配法的城乡居民候诊时间差异性评价研究

目的以候诊时间为例,探讨某地区城乡居民候诊时间的差异性,进一步丰富基本公共卫生服务项目实施效果评价的方法。方法随机抽取某地区城乡居民1395例,采用logistic回归模型计算基本公共卫生服务项目实施后城乡分组间的倾向指数,然后按照倾向指数进行组间卡钳匹配,对匹配后的数据进行统计学分析。结果经倾向指数卡钳匹配后,年龄、文化程度、保险等协变量达到了均衡;农村组与城镇组基本公共卫生服务项目实施前后候诊时间均有统计学差异(P0.001);基本公共卫生服务项目实施前,农村组与城镇组候诊时间有统计学差异(P0.001);基本公共卫生服务项目实施后,农村组与城镇组候诊时间无统计学差异(P=0.0620.05)。结论基本公共卫生服务项目实施后,城乡居民的候诊时间均相应减少,并且城乡间候诊时间趋于一致。在候诊时间指标上,基本公共卫生服务项目实施效果明显,并且在一定程度上促进了城乡区域间医疗卫生的协同发展。     

非随机化医学研究中风险比的一种估计方法

目的提出一种适用于非随机化医学研究的,结合倾向指数与非参数生存分析估计风险比的方法.方法首先对倾向指数进行估计,然后对倾向指数分布分层以消除比较两组间协变量分布的不均衡.其次对分层样本用非参数生存分析的方法估计两组间发病或死亡的风险比.最后比较本法与常用的Cox模型方法并探讨其适用性.结果将本法应用于一项评价某降血脂新药效果的4期临床试验数据后显示:(1)对倾向指数分布分层后基本上消除了由于随机分组方案失败导致的新药组与传统药物组之间协变量分布的不均衡性,使得非参数生存分析方法得以应用;(2)由本法得到的新药效果的估计-风险比与由Cox模型得到的结果基本一致.结论对于非随机化医学研究,结合倾向指数进行非参数生存分析是一种新的可选择的统计方法.     

Estimation of propensity scores using generalized additive models

Propensity score matching is often used in observational studies to create treatment and control groups with similar distributions of observed covariates. Typically, propensity scores are estimated using logistic regressions that assume linearity between the logistic link and the predictors. We evaluate the use of generalized additive models (GAMs) for estimating propensity scores. We compare logistic regressions and GAMs in terms of balancing covariates using simulation studies with artificial and genuine data. We find that, when the distributions of covariates in the treatment and control groups overlap sufficiently, using GAMs can improve overall covariate balance, especially for higher-order moments of distributions. When the distributions in the two groups overlap insufficiently, GAM more clearly reveals this fact than logistic regression does. We also demonstrate via simulation that matching with GAMs can result in larger reductions in bias when estimating treatment effects than matching with logistic regression.     

A matching method for improving covariate balance in cost-effectiveness analyses

In cost-effectiveness analyses (CEA) that use randomized controlled trials (RCTs), covariates of prognostic importance may be imbalanced and warrant adjustment. In CEA that use non-randomized studies (NRS), the selection on observables assumption must hold for regression and matching methods to be unbiased. Even in restricted circumstances when this assumption is plausible, a key concern is how to adjust for imbalances in observed confounders. If the propensity score is misspecified, the covariates in the matched sample will be imbalanced, which can lead to conditional bias. To address covariate imbalance in CEA based on RCTs and NRS, this paper considers Genetic Matching. This matching method uses a search algorithm to directly maximize covariate balance. We compare Genetic and propensity score matching in Monte Carlo simulations and two case studies, CEA of pulmonary artery catheterization, based on an RCT and an NRS. The simulations show that Genetic Matching reduces the conditional bias and root mean squared error compared with propensity score matching. Genetic Matching achieves better covariate balance than the unadjusted analyses of the RCT data. In the NRS, Genetic Matching improves on the balance obtained from propensity score matching and gives substantively different estimates of incremental cost-effectiveness. We conclude that Genetic Matching can improve balance on measured covariates in CEA that use RCTs and NRS, but with NRS, this will be insufficient to reduce bias; the selection on observables assumption must also hold.     

Squeezing the Balloon: Propensity Scores and Unmeasured Covariate Balance

Objective

To assess the covariate balancing properties of propensity score-based algorithms in which covariates affecting treatment choice are both measured and unmeasured.

Data Sources/Study Setting

A simulation model of treatment choice and outcome.

Study Design

Simulation.

Data Collection/Extraction Methods

Eight simulation scenarios varied with the values placed on measured and unmeasured covariates and the strength of the relationships between the measured and unmeasured covariates. The balance of both measured and unmeasured covariates was compared across patients either grouped or reweighted by propensity scores methods.

Principal Findings

Propensity score algorithms require unmeasured covariate variation that is unrelated to measured covariates, and they exacerbate the imbalance in this variation between treated and untreated patients relative to the full unweighted sample.

Conclusions

The balance of measured covariates between treated and untreated patients has opposite implications for unmeasured covariates in randomized and observational studies. Measured covariate balance between treated and untreated patients in randomized studies reinforces the notion that all covariates are balanced. In contrast, forced balance of measured covariates using propensity score methods in observational studies exacerbates the imbalance in the independent portion of the variation in the unmeasured covariates, which can be likened to squeezing a balloon. If the unmeasured covariates affecting treatment choice are confounders, propensity score methods can exacerbate the bias in treatment effect estimates.     

Evaluation of subset matching methods and forms of covariate balance

This paper conducts a Monte Carlo simulation study to evaluate the performance of multivariate matching methods that select a subset of treatment and control observations. The matching methods studied are the widely used nearest neighbor matching with propensity score calipers and the more recently proposed methods, optimal matching of an optimally chosen subset and optimal cardinality matching. The main findings are: (i) covariate balance, as measured by differences in means, variance ratios, Kolmogorov–Smirnov distances, and cross‐match test statistics, is better with cardinality matching because by construction it satisfies balance requirements; (ii) for given levels of covariate balance, the matched samples are larger with cardinality matching than with the other methods; (iii) in terms of covariate distances, optimal subset matching performs best; (iv) treatment effect estimates from cardinality matching have lower root‐mean‐square errors, provided strong requirements for balance, specifically, fine balance, or strength‐k balance, plus close mean balance. In standard practice, a matched sample is considered to be balanced if the absolute differences in means of the covariates across treatment groups are smaller than 0.1 standard deviations. However, the simulation results suggest that stronger forms of balance should be pursued in order to remove systematic biases due to observed covariates when a difference in means treatment effect estimator is used. In particular, if the true outcome model is additive, then marginal distributions should be balanced, and if the true outcome model is additive with interactions, then low‐dimensional joints should be balanced. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

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.     

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.     

Metrics for covariate balance in cohort studies of causal effects

Inferring causation from non‐randomized studies of exposure requires that exposure groups can be balanced with respect to prognostic factors for the outcome. Although there is broad agreement in the literature that balance should be checked, there is confusion regarding the appropriate metric. We present a simulation study that compares several balance metrics with respect to the strength of their association with bias in estimation of the effect of a binary exposure on a binary, count, or continuous outcome. The simulations utilize matching on the propensity score with successively decreasing calipers to produce datasets with varying covariate balance. We propose the post‐matching C‐statistic as a balance metric and found that it had consistently strong associations with estimation bias, even when the propensity score model was misspecified, as long as the propensity score was estimated with sufficient study size. This metric, along with the average standardized difference and the general weighted difference, outperformed all other metrics considered in association with bias, including the unstandardized absolute difference, Kolmogorov–Smirnov and Lévy distances, overlapping coefficient, Mahalanobis balance, and L₁ metrics. Of the best‐performing metrics, the C‐statistic and general weighted difference also have the advantage that they automatically evaluate balance on all covariates simultaneously and can easily incorporate balance on interactions among covariates. Therefore, when combined with the usual practice of comparing individual covariate means and standard deviations across exposure groups, these metrics may provide useful summaries of the observed covariate imbalance. Copyright © 2013 John Wiley & Sons, Ltd.