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删除14字节 、 2021年5月28日 (五) 16:57
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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'''<font color="#32cd32"> 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)】</font>''' ,并且可以控制协变量。在特定情况下,可以使用'''<font color="#32cd32"> 配对差异检验【paired difference test】 </font>'''、 McNemar 检验和 Cochran-Mantel-Haenzel 检验等更简单的检验。
 
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'''<font color="#32cd32"> 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)】</font>''' ,并且可以控制协变量。在特定情况下,可以使用'''<font color="#32cd32"> 配对差异检验【paired difference test】 </font>'''、 McNemar 检验和 Cochran-Mantel-Haenzel 检验等更简单的检验。
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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.
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当感兴趣的结果是连续的,估计的平均治疗效果进行。
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When the outcome of interest is continuous, estimation of the [[average treatment effect]] is performed.
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当感兴趣的结果是连续的,平均处理效应。
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Matching can also be used to "pre-process" a sample before analysis via another technique, such as regression analysis.
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匹配也可以用于“预处理”样品,然后再通过另一种技术进行分析,例如回归分析分析。
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Matching can also be used to "pre-process" a sample before analysis via another technique, such as [[regression analysis]].<ref>{{cite journal |last1=Ho |first1=Daniel E. |first2=Kosuke |last2=Imai |first3=Gary |last3=King |first4=Elizabeth A. |last4=Stuart |author4-link= Elizabeth A. Stuart |year=2007 |title=Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference |journal=Political Analysis |volume=15 |issue=3 |pages=199–236 |doi=10.1093/pan/mpl013 |doi-access=free }}</ref>
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Matching can also be used to "pre-process" a sample before analysis via another technique, such as regression analysis.
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When the outcome of interest is continuous, estimation of the [[average treatment effect]] is performed.
+
匹配也可以用于“预处理”样品,然后再通过另一种技术进行分析,例如回归分析分析。
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Matching can also be used to "pre-process" a sample before analysis via another technique, such as [[regression analysis]].<ref>{{cite journal |last1=Ho |first1=Daniel E. |first2=Kosuke |last2=Imai |first3=Gary |last3=King |first4=Elizabeth A. |last4=Stuart |author4-link= Elizabeth A. Stuart |year=2007 |title=Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference |journal=Political Analysis |volume=15 |issue=3 |pages=199–236 |doi=10.1093/pan/mpl013 |doi-access=free }}</ref>
      
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.
 
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.
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