ﻻ يوجد ملخص باللغة العربية
Causal inference of treatment effects is a challenging undertaking in it of itself; inference for sequential treatments leads to even more hurdles. In precision medicine, one additional ambitious goal may be to infer about effects of dynamic treatment regimes (DTRs) and to identify optimal DTRs. Conventional methods for inferring about DTRs involve powerful semi-parametric estimators. However, these are not without their strong assumptions. Dynamic Marginal Structural Models (MSMs) are one semi-parametric approach used to infer about optimal DTRs in a family of regimes. To achieve this, investigators are forced to model the expected outcome under adherence to a DTR in the family; relatively straightforward models may lead to bias in the optimum. One way to obviate this difficulty is to perform a grid search for the optimal DTR. Unfortunately, this approach becomes prohibitive as the complexity of regimes considered increases. In recently developed Bayesian methods for dynamic MSMs, computational challenges may be compounded by the fact that at each grid point, a posterior mean must be calculated. We propose a manner by which to alleviate modelling difficulties for DTRs by using Gaussian process optimization. More precisely, we show how to pair this optimization approach with robust estimators for the causal effect of adherence to a DTR to identify optimal DTRs. We examine how to find the optimum in complex, multi-modal settings which are not generally addressed in the DTR literature. We further evaluate the sensitivity of the approach to a variety of modeling assumptions in the Gaussian process.
In clinical practice, physicians make a series of treatment decisions over the course of a patients disease based on his/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes
We present a novel Graphical Multi-fidelity Gaussian Process (GMGP) model that uses a directed acyclic graph to model dependencies between multi-fidelity simulation codes. The proposed model is an extension of the Kennedy-OHagan model for problems wh
There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Because confounding by unmeasured factors cannot generally
Estimating dynamic treatment regimes (DTRs) from retrospective observational data is challenging as some degree of unmeasured confounding is often expected. In this work, we develop a framework of estimating properly defined optimal DTRs with a time-
We derive new estimators of an optimal joint testing and treatment regime under the no direct effect (NDE) assumption that a given laboratory, diagnostic, or screening test has no effect on a patients clinical outcomes except through the effect of th