目的利用真实世界医疗数据,针对心脏病患者研究硝苯地平和美托洛尔药物在主要心血管事件、全因死亡和药源性肝损伤中的作用。方法基于齐鲁全生命周期电子研究型数据库(Cheeloo Lifespan Electronic Health Research Data-library,Cheelo...目的利用真实世界医疗数据,针对心脏病患者研究硝苯地平和美托洛尔药物在主要心血管事件、全因死亡和药源性肝损伤中的作用。方法基于齐鲁全生命周期电子研究型数据库(Cheeloo Lifespan Electronic Health Research Data-library,Cheeloo LEAD),选取2012年1月1日至2022年12月31日期间首次诊断为心脏病的患者作为研究对象,采用倾向性评分匹配的新使用者队列设计,开展真实世界研究。通过大规模L1正则化倾向性评分方法进行协变量匹配,实现拟随机化处理,采用Cox比例风险回归模型评估硝苯地平(暴露组)和美托洛尔(对照组)分别对主要心血管事件、全因死亡和药源性肝损伤的平均因果效应。结果男性心脏病患者更易出现主要心血管事件、全因死亡和药源性肝损伤。L1正则化倾向性评分匹配后,协变量标准化差异全部在0.2以下且绝大部分在0.1以下,暴露组和对照组的生存曲线无明显交叉。药源性肝损伤与全因死亡的发生率在组间比较中,log-rank检验结果均具有统计学意义(P<0.05)。此外,使用硝苯地平的患者在随访期内的累积生存率高于使用美托洛尔的患者。Cox比例风险回归模型显示,硝苯地平较美托洛尔对药源性肝损伤风险更低(HR=0.39,95%CI:0.16~0.93,P=0.025)且具有更低的全因死亡风险(HR=0.53,95%CI:0.28~0.98,P=0.034),未发现硝苯地平和美托洛尔预防主要心血管事件的效果差异(HR=1.30,95%CI:0.97~1.90,P=0.061)。结论相较于美托洛尔,硝苯地平可降低心脏病患者的全国死亡风险,且其药源性肝损伤的风险更低,本研究结果可以为心脏病患者安全用药提供补充证据,尤其可为评估药源性肝损伤风险提供参考。展开更多
幸存者平均因果效应(Survivor Average Causal Effect,SACE)可以用来度量任何处理下都能存活的受试者接受不同处理的影响差异,是因果推断中的一个重要研究方向。由于处理组和对照组中总是存活的受试者样本不能直接观测,SACE通常是不可...幸存者平均因果效应(Survivor Average Causal Effect,SACE)可以用来度量任何处理下都能存活的受试者接受不同处理的影响差异,是因果推断中的一个重要研究方向。由于处理组和对照组中总是存活的受试者样本不能直接观测,SACE通常是不可识别的,只能得到SACE的边界。已有文献中SACE尖锐边界的主流求解方法依赖于多参数线性规划,通过枚举对偶问题的约束多边形的所有顶点来产生封闭形式的解。如果单调性和随机占优等条件不成立,则无法采用枚举法求解该多参数线性规划问题。文章基于主分层框架考虑了“死亡截断”、稳定个体处理效应和可忽略性假设下SACE的尖锐边界问题,其中,优化问题的求解是基于一阶KKT(Kraush-Kuhn-Tucker)条件所对应的多项式方程组。实证选取美国国家支持工作示范项目(National Supported Work Demonstration,NSW)中的Lalonde数据集,计算了“永远幸存者”(always-survivor)在完整协变量情形下的SACE尖锐边界。展开更多
在随机处理─对照的临床试验中,经常出现不依从或部分依从的现象,此时,由于所涉及到的"虚拟事实"变量,即不能观察到的潜在变量太多而不易估计其平均因果效应ACE.在仅出现完全依从和不依从情况时,Balke and Pearl利用线性规划...在随机处理─对照的临床试验中,经常出现不依从或部分依从的现象,此时,由于所涉及到的"虚拟事实"变量,即不能观察到的潜在变量太多而不易估计其平均因果效应ACE.在仅出现完全依从和不依从情况时,Balke and Pearl利用线性规划的方法获得了ACE估计量的上下界,利用他们所提供的方法,有时会出现下界为负数,显然,这样的下界没什么实际意义.根据Angrist,Imbebns&Rubin讨论工具变量时所提出一些假设条件,导出了在不同情况下,计算ACE估计量的上下界的方法,并证明了其下界一定是非负的,所得到的上下界区间比Balke and Pearl的区间要小.同时,还讨论了部分依从情况下,ACE估计量的上下界的计算方法,并得到了相应的结果.展开更多
在随机处理——对照的临床试验中,除出现完全依从和完全不依从的现象外,还会出现部分依从的现象,即患者只服用部分药品。在仅出现完全依从和不依从情况时,Balke and Pearl利用线性规划的方法获得了ACE估计量的上下界,对于部分依从的情况...在随机处理——对照的临床试验中,除出现完全依从和完全不依从的现象外,还会出现部分依从的现象,即患者只服用部分药品。在仅出现完全依从和不依从情况时,Balke and Pearl利用线性规划的方法获得了ACE估计量的上下界,对于部分依从的情况,是将这些数据全部并入完全依从的数据,这样处理的合理性没有论述。同时,利用他们所提供的方法,有时会出现下界为负数,显然,这样的下界没什么实际意义。本文根据Angrist,Imbebns&Rubin讨论工具变量时所提出一些假设条件,导出了在部分依从情况下,计算ACE估计量的上下界的方法,并证明了其下界一定是非负的。展开更多
目的利用SAS开发的CAUSALTRT过程,实现三类估计方法的因果效应估计。方法采用SmokingWeight数据集,以戒烟为处理变量,体重变化为结局变量,其他因素为混杂变量,通过增强逆概率加权法(augmented inverse probability weighting,AIPW)对平...目的利用SAS开发的CAUSALTRT过程,实现三类估计方法的因果效应估计。方法采用SmokingWeight数据集,以戒烟为处理变量,体重变化为结局变量,其他因素为混杂变量,通过增强逆概率加权法(augmented inverse probability weighting,AIPW)对平均处理效应(the average treatment effect,ATE)进行估计,通过回归调整法(regression adjustment,REGADJ)对处理组平均处理效应(the average treatment effect for the treated,ATT)进行估计。结果戒烟对体重变化的ATE和ATT分别为3.209(95%CI:2.232~4.187)和3.276(95%CI:2.332~4.219)。结论CAUSALTRT可以实现不同的因果效应估计,但应用时需要考虑其是否满足前提假设以及注意事项。展开更多
This paper considers the problem of estimating the bounds on the average controlled direct effects （ACDEs） of a treatment variable on an unobserved response variable in the presence of unobserved confounders between...This paper considers the problem of estimating the bounds on the average controlled direct effects （ACDEs） of a treatment variable on an unobserved response variable in the presence of unobserved confounders between an intermediate variable and the response variable. When the response variable is observed, Cai, et al.（2008） derived the formulas for the sharp bounds on the ACDEs. When the response variable is unobserved, the authors propose a graphical criterion for selecting variables affected by the response variable to derive the formulas for the bounds on the ACDEs, which is an extension of the result of Kuroki（2005） to ACDEs. The results enable us not only to judge from the graph structure whether the bounds on the ACDEs can be expressed through observed variables when the response variable is unobserved, but also to provide their formulas when the answer is affirmative.展开更多
This study aims to assess the Average Treatment Effect(ATE)of receiving special education services on revised Item Response Theory(IRT)scaled math achievement test scores.By employing a methodological repertoire compr...This study aims to assess the Average Treatment Effect(ATE)of receiving special education services on revised Item Response Theory(IRT)scaled math achievement test scores.By employing a methodological repertoire comprising linear regression with ordinary least squares(OLS),propensity score matching(PSM),Bayesian Additive Regression Trees(BART),and Multilayer Perceptron(MLP),we examine the impact of these interventions.Leveraging data from the Early Childhood Longitudinal Study Kindergarten 2010-11 cohort(ECLS-K:2011),we systematically analyze the ATE of special education services on students'math achievement.The results show that all models yield negative ATE results,suggesting a deleterious effect of special education services on fifth-grade math scores.Furthermore,we employ Principal Component Analysis(PCA)to corroborate these findings,aligning with outcomes obtained from causal inference and Machine Learning(ML)based methods.This research emphasizes the importance of method diversity in educational research and highlights the need for assessments of intervention effectiveness to help educational practices and policies.展开更多
Doubly robust(DR)methods that employ both the propensity score and outcome models are widely used to estimate the causal effect of a treatment and generally outperform those methods only using the propensity score or ...Doubly robust(DR)methods that employ both the propensity score and outcome models are widely used to estimate the causal effect of a treatment and generally outperform those methods only using the propensity score or the outcome model.However,without appropriately chosen the working models,DR estimators may substantially lose efficiency.In this paper,based on the augmented inverse probability weighting procedure,we derive a new estimating equation for the causal effect by the strategy of combining estimating equations.The resulting estimator by solving the new estimating equation retains doubly robust and can improve the efficiency under the misspecification of conditional mean working model.We further show the large sample properties of the proposed estimator under some regularity conditions.Through simulation experiments and a real data analysis,we illustrate that the proposed method is competitive with its competitors,which is in line with those implied by the asymptotic theory.展开更多
文摘目的利用真实世界医疗数据,针对心脏病患者研究硝苯地平和美托洛尔药物在主要心血管事件、全因死亡和药源性肝损伤中的作用。方法基于齐鲁全生命周期电子研究型数据库(Cheeloo Lifespan Electronic Health Research Data-library,Cheeloo LEAD),选取2012年1月1日至2022年12月31日期间首次诊断为心脏病的患者作为研究对象,采用倾向性评分匹配的新使用者队列设计,开展真实世界研究。通过大规模L1正则化倾向性评分方法进行协变量匹配,实现拟随机化处理,采用Cox比例风险回归模型评估硝苯地平(暴露组)和美托洛尔(对照组)分别对主要心血管事件、全因死亡和药源性肝损伤的平均因果效应。结果男性心脏病患者更易出现主要心血管事件、全因死亡和药源性肝损伤。L1正则化倾向性评分匹配后,协变量标准化差异全部在0.2以下且绝大部分在0.1以下,暴露组和对照组的生存曲线无明显交叉。药源性肝损伤与全因死亡的发生率在组间比较中,log-rank检验结果均具有统计学意义(P<0.05)。此外,使用硝苯地平的患者在随访期内的累积生存率高于使用美托洛尔的患者。Cox比例风险回归模型显示,硝苯地平较美托洛尔对药源性肝损伤风险更低(HR=0.39,95%CI:0.16~0.93,P=0.025)且具有更低的全因死亡风险(HR=0.53,95%CI:0.28~0.98,P=0.034),未发现硝苯地平和美托洛尔预防主要心血管事件的效果差异(HR=1.30,95%CI:0.97~1.90,P=0.061)。结论相较于美托洛尔,硝苯地平可降低心脏病患者的全国死亡风险,且其药源性肝损伤的风险更低,本研究结果可以为心脏病患者安全用药提供补充证据,尤其可为评估药源性肝损伤风险提供参考。
文摘幸存者平均因果效应(Survivor Average Causal Effect,SACE)可以用来度量任何处理下都能存活的受试者接受不同处理的影响差异,是因果推断中的一个重要研究方向。由于处理组和对照组中总是存活的受试者样本不能直接观测,SACE通常是不可识别的,只能得到SACE的边界。已有文献中SACE尖锐边界的主流求解方法依赖于多参数线性规划,通过枚举对偶问题的约束多边形的所有顶点来产生封闭形式的解。如果单调性和随机占优等条件不成立,则无法采用枚举法求解该多参数线性规划问题。文章基于主分层框架考虑了“死亡截断”、稳定个体处理效应和可忽略性假设下SACE的尖锐边界问题,其中,优化问题的求解是基于一阶KKT(Kraush-Kuhn-Tucker)条件所对应的多项式方程组。实证选取美国国家支持工作示范项目(National Supported Work Demonstration,NSW)中的Lalonde数据集,计算了“永远幸存者”(always-survivor)在完整协变量情形下的SACE尖锐边界。
文摘在随机处理─对照的临床试验中,经常出现不依从或部分依从的现象,此时,由于所涉及到的"虚拟事实"变量,即不能观察到的潜在变量太多而不易估计其平均因果效应ACE.在仅出现完全依从和不依从情况时,Balke and Pearl利用线性规划的方法获得了ACE估计量的上下界,利用他们所提供的方法,有时会出现下界为负数,显然,这样的下界没什么实际意义.根据Angrist,Imbebns&Rubin讨论工具变量时所提出一些假设条件,导出了在不同情况下,计算ACE估计量的上下界的方法,并证明了其下界一定是非负的,所得到的上下界区间比Balke and Pearl的区间要小.同时,还讨论了部分依从情况下,ACE估计量的上下界的计算方法,并得到了相应的结果.
文摘在随机处理——对照的临床试验中,除出现完全依从和完全不依从的现象外,还会出现部分依从的现象,即患者只服用部分药品。在仅出现完全依从和不依从情况时,Balke and Pearl利用线性规划的方法获得了ACE估计量的上下界,对于部分依从的情况,是将这些数据全部并入完全依从的数据,这样处理的合理性没有论述。同时,利用他们所提供的方法,有时会出现下界为负数,显然,这样的下界没什么实际意义。本文根据Angrist,Imbebns&Rubin讨论工具变量时所提出一些假设条件,导出了在部分依从情况下,计算ACE估计量的上下界的方法,并证明了其下界一定是非负的。
文摘目的利用SAS开发的CAUSALTRT过程,实现三类估计方法的因果效应估计。方法采用SmokingWeight数据集,以戒烟为处理变量,体重变化为结局变量,其他因素为混杂变量,通过增强逆概率加权法(augmented inverse probability weighting,AIPW)对平均处理效应(the average treatment effect,ATE)进行估计,通过回归调整法(regression adjustment,REGADJ)对处理组平均处理效应(the average treatment effect for the treated,ATT)进行估计。结果戒烟对体重变化的ATE和ATT分别为3.209(95%CI:2.232~4.187)和3.276(95%CI:2.332~4.219)。结论CAUSALTRT可以实现不同的因果效应估计,但应用时需要考虑其是否满足前提假设以及注意事项。
基金This research was partially supported by the National Natural Science Foundation of China under Grant Nos. 10871038, 10926186, and 11025102, the National 973 Key Project of China under Grant No. 2007CB311002, and the Jilin Project （20100401）.
文摘This paper considers the problem of estimating the bounds on the average controlled direct effects （ACDEs） of a treatment variable on an unobserved response variable in the presence of unobserved confounders between an intermediate variable and the response variable. When the response variable is observed, Cai, et al.（2008） derived the formulas for the sharp bounds on the ACDEs. When the response variable is unobserved, the authors propose a graphical criterion for selecting variables affected by the response variable to derive the formulas for the bounds on the ACDEs, which is an extension of the result of Kuroki（2005） to ACDEs. The results enable us not only to judge from the graph structure whether the bounds on the ACDEs can be expressed through observed variables when the response variable is unobserved, but also to provide their formulas when the answer is affirmative.
文摘This study aims to assess the Average Treatment Effect(ATE)of receiving special education services on revised Item Response Theory(IRT)scaled math achievement test scores.By employing a methodological repertoire comprising linear regression with ordinary least squares(OLS),propensity score matching(PSM),Bayesian Additive Regression Trees(BART),and Multilayer Perceptron(MLP),we examine the impact of these interventions.Leveraging data from the Early Childhood Longitudinal Study Kindergarten 2010-11 cohort(ECLS-K:2011),we systematically analyze the ATE of special education services on students'math achievement.The results show that all models yield negative ATE results,suggesting a deleterious effect of special education services on fifth-grade math scores.Furthermore,we employ Principal Component Analysis(PCA)to corroborate these findings,aligning with outcomes obtained from causal inference and Machine Learning(ML)based methods.This research emphasizes the importance of method diversity in educational research and highlights the need for assessments of intervention effectiveness to help educational practices and policies.
基金supported by theNational Natural Science Foundation of China(No.11771032 and No.11971045)Natural Science Foundation of Beijing(No.1202001)+1 种基金supported by the National Natural Science Foundation of China(No.11871001,No.12131006 and No.11971001)the Fundamental Research Funds for the Central Universities(2019NTSS18).
文摘Doubly robust(DR)methods that employ both the propensity score and outcome models are widely used to estimate the causal effect of a treatment and generally outperform those methods only using the propensity score or the outcome model.However,without appropriately chosen the working models,DR estimators may substantially lose efficiency.In this paper,based on the augmented inverse probability weighting procedure,we derive a new estimating equation for the causal effect by the strategy of combining estimating equations.The resulting estimator by solving the new estimating equation retains doubly robust and can improve the efficiency under the misspecification of conditional mean working model.We further show the large sample properties of the proposed estimator under some regularity conditions.Through simulation experiments and a real data analysis,we illustrate that the proposed method is competitive with its competitors,which is in line with those implied by the asymptotic theory.
