In real-world healthcare problems, there are often multiple competing outcomes of interest, such as treatment efficacy and side effect severity.
Offline model selection (OMS), that is, choosing the best policy from a set of many policies given only logged data, is crucial for applying offline RL in real-world settings.
Due to the lack of online interaction with the environment, offline RL is facing the following two significant challenges: (i) the agent may be confounded by the unobserved state variables; (ii) the offline data collected a prior does not provide sufficient coverage for the environment.
The classical Cox model emerged in 1972 promoting breakthroughs in how patient prognosis is quantified using time-to-event analysis in biomedicine.
In this comment, we would like to discuss a general framework unifying the MULTIFIT and other tests and compare it with the binary expansion randomized ensemble test (BERET hereafter) proposed by Lee et al. (In press).
The distance is weighted by Mutual Information (MI) which is a measure of feature relevance between the features and the class label.
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation.
1 code implementation • 27 Jan 2020 • Xiaotong Jiang, Amanda E. Nelson, Rebecca J. Cleveland, Daniel P. Beavers, Todd A. Schwartz, Liubov Arbeeva, Carolina Alvarez, Leigh F. Callahan, Stephen Messier, Richard Loeser, Michael R. Kosorok
We provide additional statistical background for the methodology developed in the clinical analysis of knee osteoarthritis in "A Precision Medicine Approach to Develop and Internally Validate Optimal Exercise and Weight Loss Treatments for Overweight and Obese Adults with Knee Osteoarthritis" (Jiang et al. 2020).
no code implementations • 13 Dec 2019 • Naim U. Rashid, Daniel J. Luckett, Jingxiang Chen, Michael T. Lawson, Longshaokan Wang, Yunshu Zhang, Eric B. Laber, Yufeng Liu, Jen Jen Yeh, Donglin Zeng, Michael R. Kosorok
PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments.
Recently, the binary expansion testing framework was introduced to test the independence of two continuous random variables by utilizing symmetry statistics that are complete sufficient statistics for dependence.
The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way.
We propose a method for constructing confidence bands for the SVM ROC curve and provide the theoretical justification for the SVM ROC curve by showing that the risk function of the estimated decision rule is uniformly consistent across the weight parameter.
Estimated composite outcomes are subsequently used to construct an estimator of an individualized treatment rule which maximizes the mean of patient-specific composite outcomes.
However, existing methods for estimating optimal dynamic treatment regimes are designed for a small number of fixed decision points occurring on a coarse time-scale.
This framework can be used to identify biclusters that differ with respect to the means of the features, the variance of the features, or more general differences.