Corpus ID: 219177037

A constrained sparse additive model for treatment effect-modifier selection

  title={A constrained sparse additive model for treatment effect-modifier selection},
  author={Hyung Park and Eva Petkova and Thaddeus Tarpey and Robert Todd Ogden},
  journal={arXiv: Methodology},
Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This paper develops a sparse additive model focused on estimation of treatment effect-modification with simultaneous treatment effect-modifier selection. We propose a version of the sparse additive model uniquely constrained to estimate the interaction effects between treatment and pretreatment covariates, while leaving the main effects of the pretreatment covariates unspecified… Expand

Figures from this paper


A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates
It is shown that coupled with an efficiency augmentation procedure, this method produces clinically meaningful estimators in a variety of settings and can be useful for practicing personalized medicine: determining from a large set of biomarkers, the subset of patients that can potentially benefit from a treatment. Expand
Sparse Additive Models
We present a new class of methods for high dimensional non-parametric regression and classification called sparse additive models. Our methods combine ideas from sparse linear modelling and additiveExpand
On Sparse representation for Optimal Individualized Treatment Selection with Penalized Outcome Weighted Learning.
This article develops a variable selection method based on penalized outcome weighted learning through which an optimal treatment rule is considered as a classification problem where each subject is weighted proportional to his or her clinical outcome. Expand
Efficient augmentation and relaxation learning for individualized treatment rules using observational data
This work considers a class of estimators for optimal treatment rules that are analogous to convex large-margin classifiers and derives rates of convergence for the proposed estimators and uses these rates to characterize the bias-variance trade-off for estimating individualized treatment rules with classification-based methods. Expand
Robust learning for optimal treatment decision with NP-dimensionality.
A robust procedure for estimating the optimal treatment regime under NP dimensionality is proposed and penalized regressions are employed with the non-concave penalty function, where the conditional mean model of the response given predictors may be misspecified. Expand
Interactions between treatment and continuous covariates: a step toward individualizing therapy.
  • P. Royston, W. Sauerbrei
  • Medicine
  • Journal of clinical oncology : official journal of the American Society of Clinical Oncology
  • 2008
The plots used by Viale et al are one way of exploring prognostic effects of continuous variables nonparametrically, but a more relevant question is that of an interaction between treatment and a continuous prognostic factor. Expand
Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens.
It is shown that AOL still yields Fisher-consistent DTRs even if the regression models are misspecified and that an appropriate choice of the augmentation guarantees smaller stochastic errors in value function estimation for AOL than the previous outcome-weighted learning. Expand
Variable selection for optimal treatment decision
A new penalized regression framework is proposed which can simultaneously estimate the optimal treatment strategy and identify important variables and greatly facilitates implementation and statistical inferences for the estimator. Expand
New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes
Two new statistical learning methods for estimating the optimal DTR are introduced, termed backward outcome weighted learning (BOWL) and simultaneous outcome weightedlearning (SOWL), and it is proved that the resulting rules are consistent, and provide finite sample bounds for the errors using the estimated rules. Expand
Estimating Individualized Treatment Rules Using Outcome Weighted Learning
This article shows that estimating an optimal ITR that is a deterministic function of patient-specific characteristics maximizing expected clinical outcome is equivalent to a classification problem where each subject is weighted proportional to his or her clinical outcome and proposes an outcome weighted learning approach based on the support vector machine framework. Expand