Collaborative targeted maximum likelihood estimation for variable importance measure: Illustration for functional outcome prediction in mild traumatic brain injuries.
Collaborative targeted maximum likelihood estimation for variable importance measure: Illustration for functional outcome prediction in mild traumatic brain injuries.
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DOI：
10.1177/0962280215627335
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发表时间：
2018-01
影响因子：
2.3
通讯作者：
TRACK-TBI Investigators including Wayne A Gordon, Hester F Lingsma, Andrew IR Maas, Pratik Mukherjee, David O Okonkwo, David M Schnyer, Alex B Valadka and Esther L Yuh
中科院分区：
文献类型：
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作者：
Pirracchio R;Yue JK;Manley GT;van der Laan MJ;Hubbard AE;TRACK-TBI Investigators including Wayne A Gordon, Hester F Lingsma, Andrew IR Maas, Pratik Mukherjee, David O Okonkwo, David M Schnyer, Alex B Valadka and Esther L Yuh
用于确定疾病竞争原因的相对重要性的标准统计实践通常依赖于特别方法，通常是机器学习过程（逐步回归，随机森林等）的副产品。因果推理框架和数据自适应方法可能有助于定制参数以匹配临床问题，并从任意建模假设中解脱出来。我们的重点是实现这种半参数方法的可变重要性度量（VIM）。我们提出了一种基于协作目标最大似然估计（cTMLE）的VIM全自动程序，这种方法可以在潜在的众多竞争原因存在的情况下优化关联的估计。我们将该方法应用于从创伤性脑损伤患者收集的数据，特别是一项前瞻性观察性研究，包括三个美国一级创伤中心。主要结果是损伤后三个月收集的残疾评分（格拉斯哥结局量表-扩展（GOSE））。我们使用基于目标最大似然估计值（TMLE）和cTMLE的变量重要性分析，在一组危险因素中确定了临床重要的预测因子。通过参数自举，我们证明了后一个过程具有基于机器学习算法的可变重要性度量的鲁棒自动估计的潜力。与TMLE相比，cTMLE估计量与更少的正偏倚和更大的95% CI覆盖率相关。这项研究证实了自动化ctml程序的强大功能，该程序可以通过机器学习来选择目标模型，以估计复杂的高维数据中的VIMs。
Standard statistical practice used for determining the relative importance of competing causes of disease typically relies on ad hoc methods, often byproducts of machine learning procedures (stepwise regression, random forest, etc.). Causal inference framework and data-adaptive methods may help to tailor parameters to match the clinical question and free one from arbitrary modeling assumptions. Our focus is on implementations of such semiparametric methods for a variable importance measure (VIM). We propose a fully automated procedure for VIM based on collaborative targeted maximum likelihood estimation (cTMLE), a method that optimizes the estimate of an association in the presence of potentially numerous competing causes. We applied the approach to data collected from traumatic brain injury patients, specifically a prospective, observational study including three US Level-1 trauma centers. The primary outcome was a disability score (Glasgow Outcome Scale – Extended (GOSE)) collected three months post-injury. We identified clinically important predictors among a set of risk factors using a variable importance analysis based on targeted maximum likelihood estimators (TMLE) and on cTMLE. Via a parametric bootstrap, we demonstrate that the latter procedure has the potential for robust automated estimation of variable importance measures based upon machine-learning algorithms. The cTMLE estimator was associated with substantially less positivity bias as compared to TMLE and larger coverage of the 95% CI. This study confirms the power of an automated cTMLE procedure that can target model selection via machine learning to estimate VIMs in complicated, high-dimensional data.
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DOI：
10.1017/s146114571300045x
发表时间：
2013-10
期刊：
The international journal of neuropsychopharmacology
影响因子：
--
作者：
Savitz J;Hodgkinson CA;Martin-Soelch C;Shen PH;Szczepanik J;Nugent AC;Herscovitch P;Grace AA;Goldman D;Drevets WC
通讯作者：
Drevets WC
影响因子：
4.8
作者：
Maas, AIR;Hukkelhoven, CWPM;Steyerberg, EW
通讯作者：
Steyerberg, EW
影响因子：
7.8
作者：
Lawford, BR;Young, R;Ritchie, T
通讯作者：
Ritchie, T
影响因子：
4.2
作者：
Teasdale, GM;Pettigrew, LEL;Jennett, B
通讯作者：
Jennett, B
DOI：
10.1097/00005373-198905000-00017
发表时间：
1989-05-01
影响因子：
--
作者：
CHAMPION, HR;SACCO, WJ;FLANAGAN, ME
通讯作者：
FLANAGAN, ME