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1.
倾向评分配比在流行病学设计中的应用   总被引:4,自引:1,他引:3  
介绍倾向评分配比法(PSM)的基本原理、具体方法,并结合实例探讨其在流行病学设计过程中的应用.PSM通过某些观察性研究某些混杂变量与研究因素的关系计算倾向评分,然后从对照组中为处理组每个个体寻找一个或多个倾向评分值相同或非常接近的个体做对照,最终使选取观察对象的混杂变量在处理组和对照组趋于均衡可比.实例分析表明.利用PSM筛选后的研究对象,主要混杂因素在两组中的偏差下降在55%以上.结论 :PSM可有效降低观察性研究的混杂偏倚,在流行病学设计阶段使用PSM可使某些观察性研究得到类似随机对照研究的效果.  相似文献
2.
目的揭示中国新型农村合作医疗制度试点对农民卫生服务利用的影响效果。方法在控制重要混杂因素的情况下采用分组与空白对照的方法进行分析性研究,通过Propensity Score Match的方法进行两组间的匹配后研究效果指标。结果自愿参合人群有一定选择性;参合对促进门诊利用总量增加有限,但对改善应就诊而未就诊的影响较明显;对促进住院服务利用总量增加明显,由于需住院人数和未住院人数的同时增加,对降低未住院率的作用不明显。不同补偿模式发挥作用不同,以补住院和门诊统筹模式的作用更好。结论试点性的新农合制度对农民卫生服务利用有一定的促进作用,作用的发挥与补偿模式紧密相关,仍需加大筹资力度以更好发挥新农合的作用。  相似文献
3.
新型农村合作医疗改善卫生服务可及性效果评价   总被引:1,自引:1,他引:0  
利用陕西省国家第四次卫生服务调查数据,采用特征分数配比法对新型农村合作医疗参保和未参保居民进行匹配,进而比较分析了参保和未参保居民卫生服务利用的总体差别以及在不同级别医疗机构就诊的差别,评价了我国新型农村合作医疗制度对于改善农村居民卫生服务可及性的效果.  相似文献
4.
新型农村合作医疗对农民门诊医疗服务利用影响的分析   总被引:1,自引:0,他引:1  
张柠 《中国卫生经济》2011,30(10):51-52
目的:利用2004年和2006年中国健康与营养调查数据集中的黑龙江省数据,分析新型农村合作医疗制度对农民门诊医疗服务利用的影响。方法:采用倍差法,以参合农民为处理组,非参合农民为对照组,比较两组农民门诊医疗服务利用的差异。结果:2006年前,新型农村合作医疗在一定程度上减少了农民的门诊医疗服务利用。结论:新型农村合作医疗制度实施初期,门诊实行家庭账户补偿形式,有利于发挥制度作用,减少了参合农民对门诊医疗服务的过度利用。  相似文献
5.
Onur Baser  PhD 《Value in health》2006,9(6):377-385
OBJECTIVE: A large number of possible techniques are available when conducting matching procedures, yet coherent guidelines for selecting the most appropriate application do not yet exist. In this article we evaluate several matching techniques and provide a suggested guideline for selecting the best technique. METHODS: The main purpose of a matching procedure is to reduce selection bias by increasing the balance between the treatment and control groups. The following approach, consisting of five quantifiable steps, is proposed to check for balance: 1) Using two sample t-statistics to compare the means of the treatment and control groups for each explanatory variable; 2) Comparing the mean difference as a percentage of the average standard deviations; 3) Comparing percent reduction of bias in the means of the explanatory variables before and after matching; 4) Comparing treatment and control density estimates for the explanatory variables; and 5) Comparing the density estimates of the propensity scores of the control units with those of the treated units. We investigated seven different matching techniques and how they performed with regard to proposed five steps. Moreover, we estimate the average treatment effect with multivariate analysis and compared the results with the estimates of propensity score matching techniques. The Medstat MarketScan Data Base provided data for use in empirical examples of the utility of several matching methods. We conducted nearest neighborhood matching (NNM) analyses in seven ways: replacement, 2 to 1 matching, Mahalanobis matching (MM), MM with caliper, kernel matching, radius matching, and the stratification method. RESULTS: Comparing techniques according to the above criteria revealed that the choice of matching has significant effects on outcomes. Patients with asthma are compared with patients without asthma and cost of illness ranged from 2040 dollars to 4463 dollars depending on the type of matching. After matching, we looked at the insignificant differences or larger P-values in the mean values (criterion 1); low mean differences as a percentage of the average standard deviation (criterion 2); 100% reduction bias in the means of explanatory variables (criterion 3); and insignificant differences when comparing the density estimates of the treatment and control groups (criterion 4 and criterion 5). Mahalanobis matching with caliber yielded the better results according all five criteria (Mean = 4463 dollars, SD = 3252 dollars). We also applied multivariate analysis over the matched sample. This decreased the deviation in cost of illness estimates more than threefold (Mean = 4456 dollars, SD = 996 dollars). CONCLUSION: Sensitivity analysis of the matching techniques is especially important because none of the proposed methods in the literature is a priori superior to the others. The suggested joint consideration of propensity score matching and multivariate analysis offers an approach to assessing the robustness of the estimates.  相似文献
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7.
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.  相似文献
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9.
Direct comparisons of health‐related quality of life (HRQoL) outcomes between non‐randomized groups might be biased, as outcomes are confounded by imbalance in pre‐treatment patient characteristics. Such bias can be reduced by adjusting on observed covariates. This is the setting of HRQoL comparisons with reference data, where age and gender adjustment is commonly used for this purpose. However, other observed covariates can be used to lessen this bias and yield more precise estimates. The objective of this study is to show that more accurate HRQoL comparisons with reference data can be obtained, accounting for few covariates in addition to age and gender by a propensity score matching approach. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献
10.
Almost nine million children under 5 years of age die every year. Diarrhea is considered to be the second leading cause of under‐five mortality in developing countries. About one out of five deaths is caused by diarrhea. In this paper, we use the newly available data set District Level Household Survey 3 to quantify the impact of access to improved sanitation on diarrheal morbidity for children less than 5 years of age in India. Using propensity score matching, we find that access to improved sanitation reduces the risk of contracting diarrhea by 2.2 percentage points. There is considerable heterogeneity in the impacts of improved sanitation. We find statistically insignificant treatment effects for children in low or middle socioeconomic status households and for girls; however, boys and children in high socioeconomic status households experienced economically significant treatment effects. The magnitude of the treatment effect differs largely by hygiene behavior. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献
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