scholarly journals A practical illustration of the importance of realistic individualized treatment rules in causal inference

2007 ◽  
Vol 1 (0) ◽  
pp. 574-596 ◽  
Author(s):  
Oliver Bembom ◽  
Mark J. van der Laan
Keyword(s):  
Causal Inference ◽  
Individualized Treatment ◽  
Treatment Rules

scholarly journals Performance guarantees for individualized treatment rules

2011 ◽  
Vol 39 (2) ◽  
pp. 1180-1210 ◽  
Author(s):  
Min Qian ◽  
Susan A. Murphy
Keyword(s):  
Individualized Treatment ◽  
Performance Guarantees ◽  
Treatment Rules

scholarly journals Residual Weighted Learning for Estimating Individualized Treatment Rules

2017 ◽  
Vol 112 (517) ◽  
pp. 169-187 ◽  
Author(s):  
Xin Zhou ◽  
Nicole Mayer-Hamblett ◽  
Umer Khan ◽  
Michael R. Kosorok
Keyword(s):  
Individualized Treatment ◽  
Treatment Rules

Robust outcome weighted learning for optimal individualized treatment rules

2019 ◽  
Vol 29 (4) ◽  
pp. 606-624
Author(s):  
Sheng Fu ◽  
Qinying He ◽  
Sanguo Zhang ◽  
Yufeng Liu
Keyword(s):  
Individualized Treatment ◽  
Treatment Rules

scholarly journals Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules

Biometrics ◽  
2017 ◽  
Vol 74 (1) ◽  
pp. 18-26 ◽  
Author(s):  
Emily L. Butler ◽  
Eric B. Laber ◽  
Sonia M. Davis ◽  
Michael R. Kosorok
Keyword(s):  
Patient Preferences ◽  
Individualized Treatment ◽  
Treatment Rules

scholarly journals Estimating Individualized Treatment Rules Using Outcome Weighted Learning

2012 ◽  
Vol 107 (499) ◽  
pp. 1106-1118 ◽  
Author(s):  
Yingqi Zhao ◽  
Donglin Zeng ◽  
A. John Rush ◽  
Michael R. Kosorok
Keyword(s):  
Individualized Treatment ◽  
Treatment Rules

scholarly journals Multicategory Outcome Weighted Margin-based Learning for Estimating Individualized Treatment Rules

2020 ◽  
Author(s):  
Chong Zhang ◽  
Jingxiang Chen ◽  
Haoda Fu ◽  
Xuanyao He ◽  
Ying-Qi Zhao ◽  
...  
Keyword(s):  
Individualized Treatment ◽  
Treatment Rules

scholarly journals Decision making and uncertainty quantification for individualized treatments using Bayesian Additive Regression Trees

2017 ◽  
Vol 28 (4) ◽  
pp. 1079-1093 ◽  
Author(s):  
Brent R Logan ◽  
Rodney Sparapani ◽  
Robert E McCulloch ◽  
Purushottam W Laud
Keyword(s):  
Uncertainty Quantification ◽  
Nonparametric Regression ◽  
Patient Outcomes ◽  
Prediction Models ◽  
Regression Trees ◽  
Individualized Treatment ◽  
Patient Specific ◽  
Additive Regression ◽  
Treatment Rules ◽  
Bayesian Additive Regression Trees

Individualized treatment rules can improve health outcomes by recognizing that patients may respond differently to treatment and assigning therapy with the most desirable predicted outcome for each individual. Flexible and efficient prediction models are desired as a basis for such individualized treatment rules to handle potentially complex interactions between patient factors and treatment. Modern Bayesian semiparametric and nonparametric regression models provide an attractive avenue in this regard as these allow natural posterior uncertainty quantification of patient specific treatment decisions as well as the population wide value of the prediction-based individualized treatment rule. In addition, via the use of such models, inference is also available for the value of the optimal individualized treatment rules. We propose such an approach and implement it using Bayesian Additive Regression Trees as this model has been shown to perform well in fitting nonparametric regression functions to continuous and binary responses, even with many covariates. It is also computationally efficient for use in practice. With Bayesian Additive Regression Trees, we investigate a treatment strategy which utilizes individualized predictions of patient outcomes from Bayesian Additive Regression Trees models. Posterior distributions of patient outcomes under each treatment are used to assign the treatment that maximizes the expected posterior utility. We also describe how to approximate such a treatment policy with a clinically interpretable individualized treatment rule, and quantify its expected outcome. The proposed method performs very well in extensive simulation studies in comparison with several existing methods. We illustrate the usage of the proposed method to identify an individualized choice of conditioning regimen for patients undergoing hematopoietic cell transplantation and quantify the value of this method of choice in relation to the optimal individualized treatment rule as well as non-individualized treatment strategies.

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