An improved method for estimation of causal effects from observational data is demonstrated. Applications in medicine have been few, and the purpose of the present study is to contribute new clinical insight by means ...An improved method for estimation of causal effects from observational data is demonstrated. Applications in medicine have been few, and the purpose of the present study is to contribute new clinical insight by means of this new and more sophisticated analysis. Long term effect of medication for adult ADHD patients is not resolved. A model with causal parameters to represent effect of medication was formulated, which accounts for time-varying confounding and selection-bias from loss to follow-up. The popular marginal structural model (MSM) for causal inference, of Robins et al., adjusts for time-varying confounding, but suffers from lack of robustness for misspecification in the weights. Recent work by Imai and Ratkovic?[1][2] achieves robustness in the MSM, through improved covariate balance (CBMSM). The CBMSM (freely available software) was compared with a standard fit of a MSM and a naive regression model, to give a robust estimate of the true treatment effect in 250 previously non-medicated adults, treated for one year, in a specialized ADHD outpatient clinic in Norway. Covariate balance was greatly improved, resulting in a stronger treatment effect than without this improvement. In terms of treatment effect per week, early stages seemed to have the strongest influence. An estimated average reduction of 4 units on the symptom scale assessed at 12 weeks, for hypothetical medication in the 9 - 12 weeks period compared to no medication in this period, was found. The treatment effect persisted throughout the whole year, with an estimated average reduction of 0.7 units per week on symptoms assessed at one year, for hypothetical medication in the last 13 weeks of the year, compared to no medication in this period. The present findings support a strong and causal direct and indirect effect of pharmacological treatment of adults with ADHD on improvement in symptoms, and with a stronger treatment effect than has been reported.展开更多
目的基于因果森林对三阴性乳腺癌(triple negative breast cancer,TNBC)患者术后放疗的疗效进行个性化评估,为个体化治疗提供决策依据。方法基于美国SEER数据库收集TNBC患者资料,根据患者全乳切除术后是否接受放疗分为两组,采用倾向性...目的基于因果森林对三阴性乳腺癌(triple negative breast cancer,TNBC)患者术后放疗的疗效进行个性化评估,为个体化治疗提供决策依据。方法基于美国SEER数据库收集TNBC患者资料,根据患者全乳切除术后是否接受放疗分为两组,采用倾向性评分匹配法得到拟随机化数据;基于因果森林探索术后放疗的个性化疗效,并识别异质性亚组。结果大约84.88%患者能够从术后放疗中获益,且具备T3~T4期或≥4个淋巴结转移或有远处转移特征的TNBC患者受益可能性较大。识别出有代表性的四个亚组,在亚组1(T1~T2分期且年龄≤59岁)和亚组3(T3~T4分期且年龄≤48岁)中术后放疗对TNBC患者5年生存率影响无统计学意义(亚组1:HR=0.98,95%CI=0.76~1.27,P=0.87;亚组3:HR=0.82,95%CI=0.56~1.21,P=0.31),在亚组2(T1~T2分期且年龄>59岁)和亚组4(T3~T4分期且年龄>48岁)中术后放疗均可以降低TNBC患者5年死亡风险(亚组2:HR=0.65,95%CI=0.48~0.89,P<0.01;亚组4:HR=0.67,95%CI=0.53~0.86,P<0.01)。结论因果森林有助于评价个性化疗效,通过因果森林能够发现三阴性乳腺癌术后放疗有特定受益患者。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
文摘An improved method for estimation of causal effects from observational data is demonstrated. Applications in medicine have been few, and the purpose of the present study is to contribute new clinical insight by means of this new and more sophisticated analysis. Long term effect of medication for adult ADHD patients is not resolved. A model with causal parameters to represent effect of medication was formulated, which accounts for time-varying confounding and selection-bias from loss to follow-up. The popular marginal structural model (MSM) for causal inference, of Robins et al., adjusts for time-varying confounding, but suffers from lack of robustness for misspecification in the weights. Recent work by Imai and Ratkovic?[1][2] achieves robustness in the MSM, through improved covariate balance (CBMSM). The CBMSM (freely available software) was compared with a standard fit of a MSM and a naive regression model, to give a robust estimate of the true treatment effect in 250 previously non-medicated adults, treated for one year, in a specialized ADHD outpatient clinic in Norway. Covariate balance was greatly improved, resulting in a stronger treatment effect than without this improvement. In terms of treatment effect per week, early stages seemed to have the strongest influence. An estimated average reduction of 4 units on the symptom scale assessed at 12 weeks, for hypothetical medication in the 9 - 12 weeks period compared to no medication in this period, was found. The treatment effect persisted throughout the whole year, with an estimated average reduction of 0.7 units per week on symptoms assessed at one year, for hypothetical medication in the last 13 weeks of the year, compared to no medication in this period. The present findings support a strong and causal direct and indirect effect of pharmacological treatment of adults with ADHD on improvement in symptoms, and with a stronger treatment effect than has been reported.
文摘目的基于因果森林对三阴性乳腺癌(triple negative breast cancer,TNBC)患者术后放疗的疗效进行个性化评估,为个体化治疗提供决策依据。方法基于美国SEER数据库收集TNBC患者资料,根据患者全乳切除术后是否接受放疗分为两组,采用倾向性评分匹配法得到拟随机化数据;基于因果森林探索术后放疗的个性化疗效,并识别异质性亚组。结果大约84.88%患者能够从术后放疗中获益,且具备T3~T4期或≥4个淋巴结转移或有远处转移特征的TNBC患者受益可能性较大。识别出有代表性的四个亚组,在亚组1(T1~T2分期且年龄≤59岁)和亚组3(T3~T4分期且年龄≤48岁)中术后放疗对TNBC患者5年生存率影响无统计学意义(亚组1:HR=0.98,95%CI=0.76~1.27,P=0.87;亚组3:HR=0.82,95%CI=0.56~1.21,P=0.31),在亚组2(T1~T2分期且年龄>59岁)和亚组4(T3~T4分期且年龄>48岁)中术后放疗均可以降低TNBC患者5年死亡风险(亚组2:HR=0.65,95%CI=0.48~0.89,P<0.01;亚组4:HR=0.67,95%CI=0.53~0.86,P<0.01)。结论因果森林有助于评价个性化疗效,通过因果森林能够发现三阴性乳腺癌术后放疗有特定受益患者。
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supported by the National Natural Science Foundation of China(72071187,11671374,71731010,71921001)Fundamental Research Funds for the Central Universities(WK3470000017,WK2040000027)。
