scholarly journals Constructing treatment selection rules based on an estimated treatment effect function: different approaches to take stochastic uncertainty into account have a substantial effect on performance

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Maren Eckert ◽  
Werner Vach
Keyword(s):  
Treatment Effect ◽  
Substantial Effect ◽  
Treatment Selection ◽  
Selection Rules ◽  
Stochastic Uncertainty ◽  
Effect Function

Inference for treatment effect parameters in potentially misspecified high-dimensional models

Biometrika ◽  
2020 ◽  
Author(s):  
Oliver Dukes ◽  
Stijn Vansteelandt
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Treatment Effect ◽  
Observational Studies ◽  
Treatment Effects ◽  
Treatment Selection ◽  
High Dimensional ◽  
Selection Mechanism ◽  
Dimensional Models ◽  
Effect Parameter

Summary Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naïve use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates grows with the sample size, correctly specifying a model for the outcome is nontrivial. In this article we deal with both of these concerns simultaneously, obtaining confidence intervals for conditional treatment effects that are uniformly valid, regardless of whether the outcome model is correct. This is done by incorporating an additional model for the treatment selection mechanism. When both models are correctly specified, we can weaken the standard conditions on model sparsity. Our procedure extends to multivariate treatment effect parameters and complex longitudinal settings.

scholarly journals Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection

2020 ◽  
Vol 11 ◽  
Author(s):  
Qiyang Ge ◽  
Xuelin Huang ◽  
Shenying Fang ◽  
Shicheng Guo ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
Random Forest ◽  
Treatment Response ◽  
Treatment Effect ◽  
Treatment Effects ◽  
State Of The Art ◽  
Treatment Selection ◽  
Individualized Treatment ◽  
Generative Adversarial Networks ◽  
Precision Oncology ◽  
Adversarial Networks

Treatment response is heterogeneous. However, the classical methods treat the treatment response as homogeneous and estimate the average treatment effects. The traditional methods are difficult to apply to precision oncology. Artificial intelligence (AI) is a powerful tool for precision oncology. It can accurately estimate the individualized treatment effects and learn optimal treatment choices. Therefore, the AI approach can substantially improve progress and treatment outcomes of patients. One AI approach, conditional generative adversarial nets for inference of individualized treatment effects (GANITE) has been developed. However, GANITE can only deal with binary treatment and does not provide a tool for optimal treatment selection. To overcome these limitations, we modify conditional generative adversarial networks (MCGANs) to allow estimation of individualized effects of any types of treatments including binary, categorical and continuous treatments. We propose to use sparse techniques for selection of biomarkers that predict the best treatment for each patient. Simulations show that MCGANs outperform seven other state-of-the-art methods: linear regression (LR), Bayesian linear ridge regression (BLR), k-Nearest Neighbor (KNN), random forest classification [RF (C)], random forest regression [RF (R)], logistic regression (LogR), and support vector machine (SVM). To illustrate their applications, the proposed MCGANs were applied to 256 patients with newly diagnosed acute myeloid leukemia (AML) who were treated with high dose ara-C (HDAC), Idarubicin (IDA) and both of these two treatments (HDAC+IDA) at M. D. Anderson Cancer Center. Our results showed that MCGAN can more accurately and robustly estimate the individualized treatment effects than other state-of-the art methods. Several biomarkers such as GSK3, BILIRUBIN, SMAC are identified and a total of 30 biomarkers can explain 36.8% of treatment effect variation.

scholarly journals A fundamental measure of treatment effect heterogeneity

2021 ◽  
Vol 9 (1) ◽  
pp. 83-108
Author(s):  
Jonathan Levy ◽  
Mark van der Laan ◽  
Alan Hubbard ◽  
Romain Pirracchio
Keyword(s):  
Treatment Effect ◽  
Mean Squared Error ◽  
Specific Treatment ◽  
Random Variable ◽  
Average Treatment Effect ◽  
Effect Function ◽  
True Variance ◽  
Targeted Maximum Likelihood ◽  
Data Adaptive ◽  
Effect Heterogeneity

Abstract The stratum-specific treatment effect function is a random variable giving the average treatment effect (ATE) for a randomly drawn stratum of potential confounders a clinician may use to assign treatment. In addition to the ATE, the variance of the stratum-specific treatment effect function is fundamental in determining the heterogeneity of treatment effect values. We offer a non-parametric plug-in estimator, the targeted maximum likelihood estimator (TMLE) and the cross-validated TMLE (CV-TMLE), to simultaneously estimate both the average and variance of the stratum-specific treatment effect function. The CV-TMLE is preferable because it guarantees asymptotic efficiency under two conditions without needing entropy conditions on the initial fits of the outcome model and treatment mechanism, as required by TMLE. Particularly, in circumstances where data adaptive fitting methods are very important to eliminate bias but hold no guarantee of satisfying the entropy condition, we show that the CV-TMLE sampling distributions maintain normality with a lower mean squared error than TMLE. In addition to verifying the theoretical properties of TMLE and CV-TMLE through simulations, we highlight some of the challenges in estimating the variance of the treatment effect, which lack double robustness and might be biased if the true variance is small and sample size insufficient.

scholarly journals Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection

2020 ◽  
Author(s):  
Qiyang Ge ◽  
Xuelin Huang ◽  
Shenying Fang ◽  
Shihcheng Guo ◽  
yuanyuan Liu ◽  
...  
Keyword(s):  
Random Forest ◽  
Treatment Response ◽  
Treatment Effect ◽  
Treatment Effects ◽  
State Of The Art ◽  
Treatment Selection ◽  
Individualized Treatment ◽  
Generative Adversarial Networks ◽  
Precision Oncology ◽  
Adversarial Networks

Treatment response is heterogeneous. However the classical methods treat the treatment response as homogeneous and estimate the average treatment effects. The traditional methods are difficult to apply to precision oncology. The artificial intelligence (AI) is a powerful tool for precision oncology. It can accurately estimate the individualized treatment effects and learn optimal treatment choices. Therefore, the AI approach can substantially improve progress and treatment outcomes of patients. As one of AI approach, conditional generative adversarial nets for inference of individualized treatment effects (GANITE) have been developed. However, the GANITE can only deal with binary treatment and does not provide a tool for optimal treatment selection. To overcome these limitations, we modify conditional generative adversarial networks (MCGANs) to allow estimation of individualized effects of any types of treatments including binary, categorical and continuous treatments. We propose to use sparse techniques for selection of biomarkers that predict the best treatment for each patient. Simulations show that the CGANs outperform seven other state-of-the-art methods: linear regression (LR), Bayesian linear ridge regression (BLR), KNN, random forest classification (RF (C)), random forest regression (RF (R)), logistic regression (LogR) and support vector machine (SVM). To illustrate their applications, the proposed CGANs were applied to 256 patients with newly diagnosed acute myeloid leukemia (AML) who were treated with high dose ara-C (HDAC), Idarubicin (IDA) and both of these two treatments (HDAC+IDA) at M. D. Anderson Cancer Center. Our results showed that the MCGAN can more accurately and robustly estimate the individualized treatment effects than other state-of-the art methods. Several biomarkers such as GSK3, BILIRUBIN, SMAC are identified and a total of 30 biomarkers can explain 36.8% of treatment effect variation.

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