Variable selection for causal mediation analysis using LASSO-based methods.
Variable selection for causal mediation analysis using LASSO-based methods.
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DOI：
10.1177/0962280221997505
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发表时间：
2021-06
影响因子：
2.3
通讯作者：
Coffman DL
中科院分区：
文献类型：
--
作者：
Ye Z;Zhu Y;Coffman DL
因果中介效应估计可以从边际结构模型中使用适当权重的逆概率加权获得。为了计算权重，需要首先拟合治疗和中介倾向评分模型。如果协变量是高维的，则可以通过包括LASSO及其变体的正则化方法来开发简约倾向评分模型。此外，在中介设置，更有效的直接或间接的效果估计，可以通过使用结果自适应LASSO选择变量的倾向评分模型，通过合并的结果信息。进行模拟研究，以评估不同的正则化方法如何影响估计的自然直接和间接效应比值比的性能。我们的模拟结果表明，通过结果自适应LASSO正则化倾向评分模型可以提高自然效应估计的效率，并通过优化协变量的平衡，在大多数情况下可以减少偏差。正则化方法，然后应用到MIMIC-III数据库，由MIT开发的ICU数据库。
Causal mediation effect estimates can be obtained from marginal structural models using inverse probability weighting with appropriate weights. In order to compute weights, treatment and mediator propensity score models need to be fitted first. If the covariates are high-dimensional, parsimonious propensity score models can be developed by regularization methods including LASSO and its variants. Furthermore, in a mediation setup, more efficient direct or indirect effect estimators can be obtained by using outcome-adaptive LASSO to select variables for propensity score models by incorporating the outcome information. A simulation study is conducted to assess how different regularization methods can affect the performance of estimated natural direct and indirect effect odds ratios. Our simulation results show that regularizing propensity score models by outcome-adaptive LASSO can improve the efficiency of the natural effect estimators and by optimizing balance in the covariates, bias can be reduced in most cases. The regularization methods are then applied to MIMIC-III database, an ICU database developed by MIT.
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影响因子：
7.6
作者：
BARON, RM;KENNY, DA
通讯作者：
KENNY, DA
DOI：
10.7326/0003-4819-127-8_part_2-199710151-00064
发表时间：
1997-10-15
影响因子：
39.2
作者：
Rubin, DB
通讯作者：
Rubin, DB
影响因子：
2.4
作者：
Hong, Guanglei;Deutsch, Jonah;Hill, Heather D.
通讯作者：
Hill, Heather D.
影响因子：
9.8
作者：
Johnson AE;Pollard TJ;Shen L;Lehman LW;Feng M;Ghassemi M;Moody B;Szolovits P;Celi LA;Mark RG
通讯作者：
Mark RG
影响因子：
2
作者：
Ghosh, Debashis;Zhu, Yeying;Coffman, Donna L.
通讯作者：
Coffman, Donna L.