“匹配”的版本间的差异
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For example, matching the control group by gestation length and/or the number of multiple births when estimating perinatal mortality and birthweight after in vitro fertilization (IVF) is overmatching, since IVF itself increases the risk of premature birth and multiple birth. | For example, matching the control group by gestation length and/or the number of multiple births when estimating perinatal mortality and birthweight after in vitro fertilization (IVF) is overmatching, since IVF itself increases the risk of premature birth and multiple birth. | ||
| − | + | 例如，在估计体外受精（IVF）后的围产期死亡率和出生体重时，按妊娠期和/或多胎数来匹配对照组就是过度匹配，因为IVF本身会增加早产和多胎的风险。 | |
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It may be regarded as a sampling bias in decreasing the external validity of a study, because the controls become more similar to the cases in regard to exposure than the general population. | It may be regarded as a sampling bias in decreasing the external validity of a study, because the controls become more similar to the cases in regard to exposure than the general population. | ||
| − | + | 它可以被看作是一个降低研究外部效度的抽样偏误，因为相比一般人群，对照组在暴露方面变得更类似于。 | |
2021年5月28日 (五) 17:16的版本
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Matching is a statistical technique which is used to evaluate the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned). The goal of matching is, for every treated unit, to find one (or more) non-treated unit(s) with similar observable characteristics against whom the effect of the treatment can be assessed. By matching treated units to similar non-treated units, matching enables a comparison of outcomes among treated and non-treated units to estimate the effect of the treatment reducing bias due to confounding.[1][2][3] Propensity score matching, an early matching technique, was developed as part of the Rubin causal model,[4] but has been shown to increase model dependence, bias, inefficiency, and power and is no longer recommended compared to other matching methods.[5]
Matching is a statistical technique which is used to evaluate the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned). The goal of matching is, for every treated unit, to find one (or more) non-treated unit(s) with similar observable characteristics against whom the effect of the treatment can be assessed. By matching treated units to similar non-treated units, matching enables a comparison of outcomes among treated and non-treated units to estimate the effect of the treatment reducing bias due to confounding. Propensity score matching, an early matching technique, was developed as part of the Rubin causal model, but has been shown to increase model dependence, bias, inefficiency, and power and is no longer recommended compared to other matching methods.
作为一种统计技术， 匹配 Matching通过在 观察研究 Observational Study或 准实验研究 Quasi-experiment（即 处理 Treatment 是非随机分配的）中比较已处理和未处理的单元，以评估处理的效果。匹配的目标是，对于每个处理单元，找到一个(或多个)具有相似可观察特征的未处理单元，以评估处理效果。通过处理单元与相似未处理单元的匹配，匹配技术可以比较处理单元与未处理单元的不同结果，从而评估处理效应，减少混杂效应带来的偏差。 倾向值匹配 Propensity Score Matching，一种早期的匹配技术，是作为 鲁宾因果模型 Rubin Causal Model的一部分发展起来的，但已被证明会增加模型依赖性、偏差、无效性和 计算量power ，与其他匹配方法相比不再推荐使用。
Matching has been promoted by Donald Rubin.[4] It was prominently criticized in economics by LaLonde (1986),[6] who compared estimates of treatment effects from an experiment to comparable estimates produced with matching methods and showed that matching methods are biased. Dehejia and Wahba (1999) reevaluated LaLonde's critique and showed that matching is a good solution.[7] Similar critiques have been raised in political science[8] and sociology[9] journals.
匹配由 唐纳德•鲁宾 Donald Rubin 推动，在经济学中主要受到 拉隆德 LaLonde（1986）的批评。LaLonde比较了一个实验中的处理效果估计和运用匹配方法产生的可比估计，表明匹配方法是有偏的。 德赫加和瓦巴 Dehejia and Wahba (1999)重新评估了LaLonde的批评，并指出匹配是一个很好的解决方案。政治学和社会学期刊上也提出了类似的批评。
Analysis
When the outcome of interest is binary, the most general tool for the analysis of matched data is conditional logistic regression as it handles strata of arbitrary size and continuous or binary treatments (predictors) and can control for covariates. In particular cases, simpler tests like paired difference test, McNemar test and Cochran-Mantel-Haenszel test are available.
When the outcome of interest is binary, the most general tool for the analysis of matched data is conditional logistic regression as it handles strata of arbitrary size and continuous or binary treatments (predictors) and can control for covariates. In particular cases, simpler tests like paired difference test, McNemar test and Cochran-Mantel-Haenszel test are available.
当感兴趣的结果是二元变量时，分析匹配数据最常用的工具是条件Logistic回归模型，因为它可以处理 任意大小的层次和连续或二元处理变量(自变量) 【strata of arbitrary size and continuous or binary treatments (predictors)】 ，并且可以控制协变量。在特定情况下，可以使用 配对差异检验【paired difference test】 、 McNemar 检验和 Cochran-Mantel-Haenzel 检验等更简单的检验。
When the outcome of interest is continuous, estimation of the average treatment effect is performed.
When the outcome of interest is continuous, estimation of the average treatment effect is performed.
当感兴趣的结果是连续的，对 平均处理效 Average Treatment Effect 应进行估计。
Matching can also be used to "pre-process" a sample before analysis via another technique, such as regression analysis.[10]
Matching can also be used to "pre-process" a sample before analysis via another technique, such as regression analysis.
匹配也可用于通过其他技术分析之前（例如回归分析）“预处理”样本。
Overmatching
Overmatching is matching for an apparent mediator that actually is a result of the exposure. If the mediator itself is stratified, an obscured relation of the exposure to the disease would highly be likely to be induced.[11] Overmatching thus causes statistical bias.[11]
Overmatching is matching for an apparent mediator that actually is a result of the exposure. If the mediator itself is stratified, an obscured relation of the exposure to the disease would highly be likely to be induced.
过匹配是对表面是是中介变量、实际上是暴露的结果进行匹配。如果中介变量本身是分层的，则很可能诱发一种模糊的疾病暴露关系。
For example, matching the control group by gestation length and/or the number of multiple births when estimating perinatal mortality and birthweight after in vitro fertilization (IVF) is overmatching, since IVF itself increases the risk of premature birth and multiple birth.[12]
For example, matching the control group by gestation length and/or the number of multiple births when estimating perinatal mortality and birthweight after in vitro fertilization (IVF) is overmatching, since IVF itself increases the risk of premature birth and multiple birth.
例如，在估计体外受精（IVF）后的围产期死亡率和出生体重时，按妊娠期和/或多胎数来匹配对照组就是过度匹配，因为IVF本身会增加早产和多胎的风险。
It may be regarded as a sampling bias in decreasing the external validity of a study, because the controls become more similar to the cases in regard to exposure than the general population.
It may be regarded as a sampling bias in decreasing the external validity of a study, because the controls become more similar to the cases in regard to exposure than the general population.
它可以被看作是一个降低研究外部效度的抽样偏误，因为相比一般人群，对照组在暴露方面变得更类似于。
See also
References
- ↑ Rubin, Donald B. (1973). "Matching to Remove Bias in Observational Studies". Biometrics. 29 (1): 159–183. doi:10.2307/2529684. JSTOR 2529684.
- ↑ Anderson, Dallas W.; Kish, Leslie; Cornell, Richard G. (1980). "On Stratification, Grouping and Matching". Scandinavian Journal of Statistics. 7 (2): 61–66. JSTOR 4615774.
- ↑ Kupper, Lawrence L.; Karon, John M.; Kleinbaum, David G.; Morgenstern, Hal; Lewis, Donald K. (1981). "Matching in Epidemiologic Studies: Validity and Efficiency Considerations". Biometrics. 37 (2): 271–291. CiteSeerX 10.1.1.154.1197. doi:10.2307/2530417. JSTOR 2530417. PMID 7272415.
- ↑ 4.0 4.1 Rosenbaum, Paul R.; Rubin, Donald B. (1983). "The Central Role of the Propensity Score in Observational Studies for Causal Effects". Biometrika. 70 (1): 41–55. doi:10.1093/biomet/70.1.41.
- ↑ King, Gary; Nielsen, Richard (October 2019). "Why Propensity Scores Should Not Be Used for Matching". Political Analysis (in English). 27 (4): 435–454. doi:10.1017/pan.2019.11. ISSN 1047-1987.
- ↑ Matching has been promoted by Donald Rubin. who compared estimates of treatment effects from an experiment to comparable estimates produced with matching methods and showed that matching methods are biased. Dehejia and Wahba (1999) reevaluated LaLonde's critique and showed that matching is a good solution. Similar critiques have been raised in political science and sociology journals. LaLonde, Robert J. (1986). "Evaluating the Econometric Evaluations of Training Programs with Experimental Data". American Economic Review. 76 (4): 604–620. JSTOR 1806062.
- ↑ Dehejia, R. H.; Wahba, S. (1999). "Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs" (PDF). Journal of the American Statistical Association. 94 (448): 1053–1062. doi:10.1080/01621459.1999.10473858.
- ↑ Arceneaux, Kevin; Gerber, Alan S.; Green, Donald P. (2006). "Comparing Experimental and Matching Methods Using a Large-Scale Field Experiment on Voter Mobilization". Political Analysis. 14 (1): 37–62. doi:10.1093/pan/mpj001.
- ↑ Arceneaux, Kevin; Gerber, Alan S.; Green, Donald P. (2010). "A Cautionary Note on the Use of Matching to Estimate Causal Effects: An Empirical Example Comparing Matching Estimates to an Experimental Benchmark". Sociological Methods & Research. 39 (2): 256–282. doi:10.1177/0049124110378098. S2CID 37012563.
- ↑ Ho, Daniel E.; Imai, Kosuke; King, Gary; Stuart, Elizabeth A. (2007). "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference". Political Analysis. 15 (3): 199–236. doi:10.1093/pan/mpl013.
- ↑ 11.0 11.1 Marsh, J. L.; Hutton, J. L.; Binks, K. (2002). "Removal of radiation dose response effects: an example of over-matching". British Medical Journal. 325 (7359): 327–330. doi:10.1136/bmj.325.7359.327. PMC 1123834. PMID 12169512.
- ↑ Gissler, M.; Hemminki, E. (1996). "The danger of overmatching in studies of the perinatal mortality and birthweight of infants born after assisted conception". Eur J Obstet Gynecol Reprod Biol. 69 (2): 73–75. doi:10.1016/0301-2115(95)02517-0. PMID 8902436.
Further reading
- Angrist, Joshua D.; Pischke, Jörn-Steffen (2009). "Regression Meets Matching". Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. pp. 69–80. ISBN 978-0-691-12034-8.
Category:Bias
类别: 偏见
Category:Design of experiments
类别: 实验设计
Category:Medical statistics
类别: 医学统计
Category:Observational study
类别: 观察性研究
Category:Sampling techniques
类别: 抽样技术
This page was moved from wikipedia:en:Matching (statistics). Its edit history can be viewed at 匹配/edithistory