Research Papers

@article{D2022,

title = {Semiparametric Bayesian inference for optimal dynamic treatment regimes via dynamic marginal structural models},

author = {D, Rodriguez Duque; D, Stephens; EEM, Moodie; MB, Klein;},

url = {https://academic.oup.com/biostatistics/advance-article-abstract/doi/10.1093/biostatistics/kxac007/6564195?redirectedFrom=fulltext&login=false},

doi = {10.1093/biostatistics/kxac007},

year  = {2022},

date = {2022-01-01},

journal = {Biostatistics},

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 to arrive at inference of DTRs. We do this (i) by linking the observational world with a world in which all patients are randomized to a DTR, thereby allowing for causal inference and then (ii) by maximizing a posterior predictive utility, where the posterior distribution has been obtained from nonparametric prior assumptions on the observational world data-generating process. Our approach relies on Bayesian semiparametric inference, where inference about a finite-dimensional 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 and the nonparametric Bayesian bootstrap. The proposed methods allow for uncertainty quantification at the individual level, thereby enabling personalized decision-making. We examine the performance of these methods via simulation and demonstrate their utility by exploring whether to adapt HIV therapy to a measure of patients' liver health, in order to minimize liver scarring.},

keywords = {Bayesian inference, Dynamic treatment regimes, Marginal structural models},

pubstate = {published},

tppubtype = {article}

}

@article{Saarela2015,

title = {On Bayesian estimation of marginal structural models},

author = {Olli Saarela and David A. Stephens and Erica E. M. Moodie and Marina B. Klein},

url = {https://www.ncbi.nlm.nih.gov/pubmed/25677103},

doi = {10.1111/biom.12269},

year  = {2015},

date = {2015-06-15},

journal = {Biometrics},

abstract = {The purpose of inverse probability of treatment (IPT) weighting in estimation of marginal treatment effects is to construct a pseudo-population without imbalances in measured covariates, thus removing the effects of confounding and informative censoring when performing inference. In this article, we formalize the notion of such a pseudo-population as a data generating mechanism with particular characteristics, and show that this leads to a natural Bayesian interpretation of IPT weighted estimation. Using this interpretation, we are able to propose the first fully Bayesian procedure for estimating parameters of marginal structural models using an IPT weighting. Our approach suggests that the weights should be derived from the posterior predictive treatment assignment and censoring probabilities, answering the question of whether and how the uncertainty in the estimation of the weights should be incorporated in Bayesian inference of marginal treatment effects. The proposed approach is compared to existing methods in simulated data, and applied to an analysis of the Canadian Co-infection Cohort.

© 2015, The International Biometric Society.},

keywords = {Bayesian inference, Causal inference, Inverse probability weighting, Longitudinal data, Marginal structural models, Posterior predictive inference, Variance estimation},

pubstate = {published},

tppubtype = {article}

}