Year: 2020
Type: editorial
Abstract: Considerable statistical work done on dynamic treatment regimes (DTRs) is in the frequentist paradigm, but Bayesian methods may have much to offer in this setting as they allow for the appropriate representation and propagation of uncertainty, including at the individual level.In this work, we extend the use of recently developed Bayesian methods for Marginal Structural Models (MSMs) to arrive at inference of DTRs.We do this 1) by linking the observational world with a world in which all patients are randomized to a DTR, thereby allowing for causal inference and then 2) by maximizing a posterior predictive utility, where the posterior distribution has been obtained from non-parametric prior assumptions on the observational world datagenerating process.Our approach relies on Bayesian semi-parametric inference, where inference about a finitedimensional parameter is made all while working within an infinite-dimensional space of distributions.We further study Bayesian inference of DTRs in the double robust setting by using posterior predictive inference
Source: Statistics in Medicine
DOI: 10.1002/sim.8825
Authors: Erica E. M. Moodie, David A. Stephens
Institution: McGill University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W3113629490