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主分层

2022-04-10T06:50:37Z

Jlyt007：本词条由用户名初步翻译

本词条由[[用户名]]初步翻译

'''Principal stratification''' is a [[statistical]] technique used in [[causal inference]] when adjusting results for post-treatment covariates. The idea is to identify underlying strata and then compute causal effects only within strata. It is a generalization of the local average treatment effect (LATE).

Principal stratification is a statistical technique used in causal inference when adjusting results for post-treatment covariates. The idea is to identify underlying strata and then compute causal effects only within strata. It is a generalization of the local average treatment effect (LATE).

主分层是一种应用于因果推断的统计技术，它根据处置后协变量来调整因果效应。其基本思想是识别潜在的分层结构，然后只计算每一层的因果效应。这就是所谓的局部平均处理效应(LATE)。

==Example==
An example of principal stratification is where there is attrition in a randomized controlled trial. With a binary post-treatment covariate (e.g. attrition) and a binary treatment (e.g. "treatment" and "control") there are four possible strata in which subjects could be:
# those who always stay in the study regardless of which treatment they were assigned
# those who would always drop-out of the study regardless of which treatment they were assigned
# those who only drop-out if assigned to the treatment group
# those who only drop-out if assigned to the control group
If the researcher knew the stratum for each subject then the researcher could compare outcomes only within the first stratum and estimate a valid causal effect for that population. The researcher does not know this information, however, so modelling assumptions are required to use this approach.

An example of principal stratification is where there is attrition in a randomized controlled trial. With a binary post-treatment covariate (e.g. attrition) and a binary treatment (e.g. "treatment" and "control") there are four possible strata in which subjects could be:
# those who always stay in the study regardless of which treatment they were assigned
# those who would always drop-out of the study regardless of which treatment they were assigned
# those who only drop-out if assigned to the treatment group
# those who only drop-out if assigned to the control group
If the researcher knew the stratum for each subject then the researcher could compare outcomes only within the first stratum and estimate a valid causal effect for that population. The researcher does not know this information, however, so modelling assumptions are required to use this approach.

主分层的一个例子是随机对照试验的退出偏移问题。使用处置后的二元协变量(例如：退出)和二元处置变量(例如：“处置”和“对照”) ，受试者可能有四种情形：

# 总是留在研究中的受试者，不管他们被分配了哪种治疗；
# 总是会退出研究的受试者，不管他们被分配了哪种治疗；
# 只有在分配到处置组时才退出的受试者；
# 只有在分配到对照组时才退出的受试者。

如果研究人员知道每个受试者属于哪种情形，那么研究人员只需比较第一种情况下的结果，并估计出对该群提有效的因果效应。然而，研究人员并不知道这些信息，因此这种方法需要模型假设。

Using the principal stratification framework also permits providing bounds for the estimated effect (under different bounding assumptions), which is common in situations with attrition.

Using the principal stratification framework also permits providing bounds for the estimated effect (under different bounding assumptions), which is common in situations with attrition.

使用主分层框架还允许为估计效应提供界限(在不同的界限假设下) ，这在退出偏移的情况下很常见。

In applied evaluation research, principal strata are commonly referred to as "endogenous" strata or "subgroups" and involve specialized methods of analysis for examining the effects of interventions or treatments in the medical and social sciences.

In applied evaluation research, principal strata are commonly referred to as "endogenous" strata or "subgroups" and involve specialized methods of analysis for examining the effects of interventions or treatments in the medical and social sciences.

在评价研究应用中，主成分层通常被称为”内生”层或”亚群体”，并涉及专门的分析方法，用来检查医学和社会科学中的干预或处置的效果。

==See also==
*[[Instrumental variable]]
*[[Rubin causal model]]

*Instrumental variable
*Rubin causal model

= = = = = = = = 工具变量 · 虚拟事实模型

==References==
{{Reflist}}

*{{Cite journal|doi=10.1111/j.0006-341X.2002.00021.x |first1=Constantine E. |last1=Frangakis |first2=Donald B. |last2=Rubin |title=Principal stratification in causal inference |journal=Biometrics |volume=58 |issue=1 |pages=21–9 |date=March 2002 |pmid=11890317|pmc=4137767 }} [http://www.biostat.jhsph.edu/~cfrangak/papers/preffects.pdf Preprint]
* Zhang, Junni L.; Rubin, Donald B. (2003) "Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by "Death"", ''Journal of Educational and Behavioral Statistics'', 28: 353–368 {{doi|10.3102/10769986028004353}}
*Barnard, John; Frangakis, Constantine E.; Hill, Jennifer L.; Rubin, Donald B. (2003) "Principal Stratification Approach to Broken Randomized Experiments", ''[[Journal of the American Statistical Association]]'', 98, 299–323 {{doi|10.1198/016214503000071}}
*Roy, Jason; Hogan, Joseph W.; [[Bess Marcus|Marcus, Bess H.]] (2008) "Principal stratification with predictors of compliance for randomized trials with 2 active treatments", ''Biostatistics'', 9 (2), 277–289. {{doi|10.1093/biostatistics/kxm027}}
*Egleston, Brian L.; Cropsey, Karen L.; Lazev, Amy B.; Heckman, Carolyn J.; (2010) "A tutorial on principal stratification-based sensitivity analysis: application to smoking cessation studies", ''Clinical Trials'', 7 (3), 286–298. {{doi|10.1177/1740774510367811 }}
* Peck, L. R.; (2013) "On estimating experimental impacts on endogenous subgroups: Part one of a methods note in three parts", ''American Journal of Evaluation'', 34 (2), 225–236. {{doi|10.1177/1098214013481666}}

* Preprint
* Zhang, Junni L.; Rubin, Donald B. (2003) "Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by "Death"", Journal of Educational and Behavioral Statistics, 28: 353–368
*Barnard, John; Frangakis, Constantine E.; Hill, Jennifer L.; Rubin, Donald B. (2003) "Principal Stratification Approach to Broken Randomized Experiments", Journal of the American Statistical Association, 98, 299–323
*Roy, Jason; Hogan, Joseph W.; Marcus, Bess H. (2008) "Principal stratification with predictors of compliance for randomized trials with 2 active treatments", Biostatistics, 9 (2), 277–289.
*Egleston, Brian L.; Cropsey, Karen L.; Lazev, Amy B.; Heckman, Carolyn J.; (2010) "A tutorial on principal stratification-based sensitivity analysis: application to smoking cessation studies", Clinical Trials, 7 (3), 286–298.
* Peck, L. R.; (2013) "On estimating experimental impacts on endogenous subgroups: Part one of a methods note in three parts", American Journal of Evaluation, 34 (2), 225–236.

* Preprint

* Zhang, Junni L.; Rubin, Donald B.(2003)“当某些结果被“死亡”截断时，通过主成分分层估计因果效应”，《教育与行为统计学杂志》，28:353-368
* Barnard，John; Frangakis，Constantine e. ; Hill，Jennifer l. ; Rubin，Donald b。(2003)“破碎随机试验的主要分层方法”，《美国统计协会杂志》，98,299-323
* Roy，Jason; Hogan，Joseph w. ; Marcus，Bess h. (2008)“2个主动治疗的随机试验的依从性的主要分层预测因子”，《生物统计学》，9(2) ，277-289。
* Egleston，Brian l. ; Cropsey，Karen l. ; Lazev，Amy b. ; Heckman，Carolyn j. ; (2010)“关于基于主要分层的敏感度分析: 应用于戒烟研究的教程”，《临床试验》，7(3) ，286-298。
* Peck，l. r. ; (2013)“ On estimating experimental impacts On endogenous subgroups: Part one of a methods note in three parts”，American Journal of Evaluation，34(2) ，225-236。

{{DEFAULTSORT:Principal Stratification}}
[[Category:Causal inference]]
[[Category:Statistical methods]]

Category:Causal inference
Category:Statistical methods

类别: 因果推理类别: 统计方法

{{statistics-stub}}

<noinclude>

<small>This page was moved from [[wikipedia:en:Principal stratification]]. Its edit history can be viewed at [[主分层/edithistory]]</small></noinclude>

[[Category:待整理页面]]

主分层

2022-04-10T06:49:55Z

Jlyt007：/* Example */ 本词条由用户名初步翻译

此词条暂由彩云小译翻译，翻译字数共396，未经人工整理和审校，带来阅读不便，请见谅。

'''Principal stratification''' is a [[statistical]] technique used in [[causal inference]] when adjusting results for post-treatment covariates. The idea is to identify underlying strata and then compute causal effects only within strata. It is a generalization of the local average treatment effect (LATE).

Principal stratification is a statistical technique used in causal inference when adjusting results for post-treatment covariates. The idea is to identify underlying strata and then compute causal effects only within strata. It is a generalization of the local average treatment effect (LATE).

主分层是一种应用于因果推断的统计技术，它根据处置后协变量来调整因果效应。其基本思想是识别潜在的分层结构，然后只计算每一层的因果效应。这就是所谓的局部平均处理效应(LATE)。

==Example==
An example of principal stratification is where there is attrition in a randomized controlled trial. With a binary post-treatment covariate (e.g. attrition) and a binary treatment (e.g. "treatment" and "control") there are four possible strata in which subjects could be:
# those who always stay in the study regardless of which treatment they were assigned
# those who would always drop-out of the study regardless of which treatment they were assigned
# those who only drop-out if assigned to the treatment group
# those who only drop-out if assigned to the control group
If the researcher knew the stratum for each subject then the researcher could compare outcomes only within the first stratum and estimate a valid causal effect for that population. The researcher does not know this information, however, so modelling assumptions are required to use this approach.

An example of principal stratification is where there is attrition in a randomized controlled trial. With a binary post-treatment covariate (e.g. attrition) and a binary treatment (e.g. "treatment" and "control") there are four possible strata in which subjects could be:
# those who always stay in the study regardless of which treatment they were assigned
# those who would always drop-out of the study regardless of which treatment they were assigned
# those who only drop-out if assigned to the treatment group
# those who only drop-out if assigned to the control group
If the researcher knew the stratum for each subject then the researcher could compare outcomes only within the first stratum and estimate a valid causal effect for that population. The researcher does not know this information, however, so modelling assumptions are required to use this approach.

主分层的一个例子是随机对照试验的退出偏移问题。使用处置后的二元协变量(例如：退出)和二元处置变量(例如：“处置”和“对照”) ，受试者可能有四种情形：

# 总是留在研究中的受试者，不管他们被分配了哪种治疗；
# 总是会退出研究的受试者，不管他们被分配了哪种治疗；
# 只有在分配到处置组时才退出的受试者；
# 只有在分配到对照组时才退出的受试者。

如果研究人员知道每个受试者属于哪种情形，那么研究人员只需比较第一种情况下的结果，并估计出对该群提有效的因果效应。然而，研究人员并不知道这些信息，因此这种方法需要模型假设。

Using the principal stratification framework also permits providing bounds for the estimated effect (under different bounding assumptions), which is common in situations with attrition.

Using the principal stratification framework also permits providing bounds for the estimated effect (under different bounding assumptions), which is common in situations with attrition.

使用主分层框架还允许为估计效应提供界限(在不同的界限假设下) ，这在退出偏移的情况下很常见。

In applied evaluation research, principal strata are commonly referred to as "endogenous" strata or "subgroups" and involve specialized methods of analysis for examining the effects of interventions or treatments in the medical and social sciences.

In applied evaluation research, principal strata are commonly referred to as "endogenous" strata or "subgroups" and involve specialized methods of analysis for examining the effects of interventions or treatments in the medical and social sciences.

在评价研究应用中，主成分层通常被称为”内生”层或”亚群体”，并涉及专门的分析方法，用来检查医学和社会科学中的干预或处置的效果。

==See also==
*[[Instrumental variable]]
*[[Rubin causal model]]

*Instrumental variable
*Rubin causal model

= = = = = = = = 工具变量 · 虚拟事实模型

==References==
{{Reflist}}

*{{Cite journal|doi=10.1111/j.0006-341X.2002.00021.x |first1=Constantine E. |last1=Frangakis |first2=Donald B. |last2=Rubin |title=Principal stratification in causal inference |journal=Biometrics |volume=58 |issue=1 |pages=21–9 |date=March 2002 |pmid=11890317|pmc=4137767 }} [http://www.biostat.jhsph.edu/~cfrangak/papers/preffects.pdf Preprint]
* Zhang, Junni L.; Rubin, Donald B. (2003) "Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by "Death"", ''Journal of Educational and Behavioral Statistics'', 28: 353–368 {{doi|10.3102/10769986028004353}}
*Barnard, John; Frangakis, Constantine E.; Hill, Jennifer L.; Rubin, Donald B. (2003) "Principal Stratification Approach to Broken Randomized Experiments", ''[[Journal of the American Statistical Association]]'', 98, 299–323 {{doi|10.1198/016214503000071}}
*Roy, Jason; Hogan, Joseph W.; [[Bess Marcus|Marcus, Bess H.]] (2008) "Principal stratification with predictors of compliance for randomized trials with 2 active treatments", ''Biostatistics'', 9 (2), 277–289. {{doi|10.1093/biostatistics/kxm027}}
*Egleston, Brian L.; Cropsey, Karen L.; Lazev, Amy B.; Heckman, Carolyn J.; (2010) "A tutorial on principal stratification-based sensitivity analysis: application to smoking cessation studies", ''Clinical Trials'', 7 (3), 286–298. {{doi|10.1177/1740774510367811 }}
* Peck, L. R.; (2013) "On estimating experimental impacts on endogenous subgroups: Part one of a methods note in three parts", ''American Journal of Evaluation'', 34 (2), 225–236. {{doi|10.1177/1098214013481666}}

* Preprint
* Zhang, Junni L.; Rubin, Donald B. (2003) "Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by "Death"", Journal of Educational and Behavioral Statistics, 28: 353–368
*Barnard, John; Frangakis, Constantine E.; Hill, Jennifer L.; Rubin, Donald B. (2003) "Principal Stratification Approach to Broken Randomized Experiments", Journal of the American Statistical Association, 98, 299–323
*Roy, Jason; Hogan, Joseph W.; Marcus, Bess H. (2008) "Principal stratification with predictors of compliance for randomized trials with 2 active treatments", Biostatistics, 9 (2), 277–289.
*Egleston, Brian L.; Cropsey, Karen L.; Lazev, Amy B.; Heckman, Carolyn J.; (2010) "A tutorial on principal stratification-based sensitivity analysis: application to smoking cessation studies", Clinical Trials, 7 (3), 286–298.
* Peck, L. R.; (2013) "On estimating experimental impacts on endogenous subgroups: Part one of a methods note in three parts", American Journal of Evaluation, 34 (2), 225–236.

* Preprint

* Zhang, Junni L.; Rubin, Donald B.(2003)“当某些结果被“死亡”截断时，通过主成分分层估计因果效应”，《教育与行为统计学杂志》，28:353-368
* Barnard，John; Frangakis，Constantine e. ; Hill，Jennifer l. ; Rubin，Donald b。(2003)“破碎随机试验的主要分层方法”，《美国统计协会杂志》，98,299-323
* Roy，Jason; Hogan，Joseph w. ; Marcus，Bess h. (2008)“2个主动治疗的随机试验的依从性的主要分层预测因子”，《生物统计学》，9(2) ，277-289。
* Egleston，Brian l. ; Cropsey，Karen l. ; Lazev，Amy b. ; Heckman，Carolyn j. ; (2010)“关于基于主要分层的敏感度分析: 应用于戒烟研究的教程”，《临床试验》，7(3) ，286-298。
* Peck，l. r. ; (2013)“ On estimating experimental impacts On endogenous subgroups: Part one of a methods note in three parts”，American Journal of Evaluation，34(2) ，225-236。

{{DEFAULTSORT:Principal Stratification}}
[[Category:Causal inference]]
[[Category:Statistical methods]]

Category:Causal inference
Category:Statistical methods

类别: 因果推理类别: 统计方法

{{statistics-stub}}

<noinclude>

<small>This page was moved from [[wikipedia:en:Principal stratification]]. Its edit history can be viewed at [[主分层/edithistory]]</small></noinclude>

[[Category:待整理页面]]