scholarly journals Assessing and communicating heterogeneity of treatment effects for patient subpopulations: Keynote and panel discussion on communicating heterogeneous treatment effects across populations

2021 ◽  
Author(s):  
James Heyward ◽  
Milena Lolic ◽  
Catherine Spong ◽  
David Atkins ◽  
Gene Pennello ◽  
...  
Keyword(s):  
Treatment Effects ◽  
Panel Discussion ◽  
Heterogeneous Treatment Effects ◽  
Heterogeneity Of Treatment Effects

Assessing and communicating heterogeneity of treatment effects for patient subpopulations: Panel discussion on considerations in design and analysis

2020 ◽  
Author(s):  
Mark Rothmann ◽  
William Crown ◽  
Thomas A. Louis ◽  
Thomas Permutt ◽  
Stephen Ruberg ◽  
...  
Keyword(s):  
Treatment Effects ◽  
Panel Discussion ◽  
Heterogeneity Of Treatment Effects

scholarly journals Demonstrating Heterogeneity of Treatment Effects Among Patients: An Overlooked but Important Step Toward Precision Medicine

2019 ◽  
Vol 106 (1) ◽  
pp. 204-210 ◽  
Author(s):  
Jennifer S. Gewandter ◽  
Michael P. McDermott ◽  
Hua He ◽  
Shan Gao ◽  
Xueya Cai ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Treatment Effects ◽  
Heterogeneity Of Treatment Effects

scholarly journals Heterogeneity of treatment effects in malignant pleural mesothelioma – Authors' reply

The Lancet ◽  
2021 ◽  
Vol 398 (10297) ◽  
pp. 302
Author(s):  
Paul Baas ◽  
Arnaud Scherpereel ◽  
Anna K Nowak ◽  
Abderrahim Oukessou ◽  
Gerard Zalcman
Keyword(s):  
Malignant Pleural Mesothelioma ◽  
Treatment Effects ◽  
Pleural Mesothelioma ◽  
Heterogeneity Of Treatment Effects

Estimating Heterogeneous Treatment Effects and the Effects of Heterogeneous Treatments with Ensemble Methods

2017 ◽  
Vol 25 (4) ◽  
pp. 413-434 ◽  
Author(s):  
Justin Grimmer ◽  
Solomon Messing ◽  
Sean J. Westwood
Keyword(s):  
Treatment Effects ◽  
Ensemble Methods ◽  
Ensemble Method ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Randomized Experiments ◽  
Heterogeneous Treatment Effects ◽  
Data Set ◽  
Heterogeneous Effects ◽  
The Ensemble Method

Randomized experiments are increasingly used to study political phenomena because they can credibly estimate the average effect of a treatment on a population of interest. But political scientists are often interested in how effects vary across subpopulations—heterogeneous treatment effects—and how differences in the content of the treatment affects responses—the response to heterogeneous treatments. Several new methods have been introduced to estimate heterogeneous effects, but it is difficult to know if a method will perform well for a particular data set. Rather than using only one method, we show how an ensemble of methods—weighted averages of estimates from individual models increasingly used in machine learning—accurately measure heterogeneous effects. Building on a large literature on ensemble methods, we show how the weighting of methods can contribute to accurate estimation of heterogeneous treatment effects and demonstrate how pooling models lead to superior performance to individual methods across diverse problems. We apply the ensemble method to two experiments, illuminating how the ensemble method for heterogeneous treatment effects facilitates exploratory analysis of treatment effects.

Potential Genetic Causes of Heterogeneity of Treatment Effects

2007 ◽  
Vol 120 (4) ◽  
pp. S21-S25 ◽  
Author(s):  
David B. Goldstein ◽  
Anna C. Need ◽  
Rinki Singh ◽  
Sanjay M. Sisodiya
Keyword(s):  
Treatment Effects ◽  
Genetic Causes ◽  
Heterogeneity Of Treatment Effects

scholarly journals Metalearners for estimating heterogeneous treatment effects using machine learning

2019 ◽  
Vol 116 (10) ◽  
pp. 4156-4165 ◽  
Author(s):  
Sören R. Künzel ◽  
Jasjeet S. Sekhon ◽  
Peter J. Bickel ◽  
Bin Yu
Keyword(s):  
Machine Learning ◽  
Treatment Effects ◽  
Field Experiments ◽  
Average Treatment Effect ◽  
Regularity Conditions ◽  
Heterogeneous Treatment Effects ◽  
Lipschitz Continuous ◽  
Additive Regression ◽  
Underlying Mechanisms ◽  
And Control

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms—such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.

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