A Single-Index Model With a Surface-Link for Optimizing Individualized Dose Rules

@article{Park2020ASM,
  title={A Single-Index Model With a Surface-Link for Optimizing Individualized Dose Rules},
  author={Hyung Park and Eva Petkova and Thaddeus Tarpey and Robert Todd Ogden},
  journal={arXiv: Methodology},
  year={2020}
}
This paper focuses on the problem of modeling and estimating interaction effects between covariates and a continuous treatment variable on an outcome, using a simple and intuitive single-index regression approach. The primary motivation is to estimate an optimal individualized dose rule in an observational study. To model possibly nonlinear interaction effects between patients' covariates and a continuous treatment variable, we employ a two-dimensional penalized spline regression on an index… Expand

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References

SHOWING 1-10 OF 84 REFERENCES
Personalized Dose Finding Using Outcome Weighted Learning
TLDR
A randomized trial design where candidate dose levels assigned to study subjects are randomly chosen from a continuous distribution within a safe range is advocated and an outcome weighted learning method based on a nonconvex loss function is proposed, which can be solved efficiently using a difference of convex functions algorithm. Expand
PERFORMANCE GUARANTEES FOR INDIVIDUALIZED TREATMENT RULES.
TLDR
The use of clinical trial data is considered in the construction of an individualized treatment rule leading to highest mean response and estimation based on l(1) penalized least squares is considered. Expand
Estimating Individualized Treatment Rules Using Outcome Weighted Learning
TLDR
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
Variable Selection for Qualitative Interactions in Personalized Medicine While Controlling the Family-Wise Error Rate
TLDR
This article proposes a new technique designed specifically to find interaction variables among a large set of variables while still controlling for the number of false discoveries and compares this new method against standard qualitative interaction tests using simulations. Expand
Estimating Optimal Treatment Regimes from a Classification Perspective.
TLDR
This work proposes a novel and general framework that transforms the problem of estimating an optimal treatment regime into a classification problem wherein the optimal classifier corresponds to the optimalreatment regime. Expand
Multivariate calibration with temperature interaction using two-dimensional penalized signal regression
Abstract The Penalized Signal Regression (PSR) approach to multivariate calibration (MVC) assumes a smooth vector of coefficients for weighting a spectrum to predict the unknown concentration of aExpand
Variable selection for optimal treatment decision
TLDR
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
Varying-coefficient single-index signal regression
Abstract The penalized signal regression (PSR) approach to multivariate calibration (MVC) assumes a smooth vector of coefficients for weighting a signal or spectrum to predict the unknownExpand
Optimal dynamic treatment regimes
A dynamic treatment regime is a list of decision rules, one per time interval, for how the level of treatment will be tailored through time to an individual's changing status. The goal of this paperExpand
Optimal Structural Nested Models for Optimal Sequential Decisions
TLDR
A novel “Bayes-frequentist compromise” is proposed that combines honest subjective non- or semiparametric Bayesian inference with good frequentist behavior, even in cases where the model is so large and the likelihood function so complex that standard Bayes procedures have poor frequentist performance. Expand
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