5 citations · 12 across the 15 of their papers we have counts for
18 papers
A Bayesian framework for multilevel data under model mis-specification
Widemberg S. Nobre, David A. Stephens, Alexandra M. Schmidt +1
We propose a Bayesian framework for uncertainty quantification from the perspective that the working model is mis-specified in settings of a multilevel data-generating process. We…
Information Borrowing from Partially Compatible Trajectories for Estimation of Dynamic Treatment Regimes
Chloe Si, David A. Stephens, Erica E. M. Moodie
Dynamic Treatment Regimes (DTRs) provide a systematic framework for optimizing sequential decision-making in chronic disease management, where therapies must adapt to patients' evo…
Individualized treatment regimens under correlated data with multiple outcomes
Misha Dolmatov, Erica E. M. Moodie, David A. Stephens +1
Precision medicine involves developing individualized treatment regimes (ITRs) which allow for treatment decisions to be tailored to patient characteristics. Naturally, the identif…
The impact of directly observed therapy on the efficacy of Tuberculosis treatment: A Bayesian multilevel approach
Widemberg S. Nobre, Alexandra M. Schmidt, Erica E. M. Moodie +1
We propose and discuss a Bayesian procedure to estimate the average treatment effect (ATE) for multilevel observations in the presence of confounding. We focus on situations where…
A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies
Armando Turchetta, Nicolas Savy, David A. Stephens +2
Forecasting recruitments is a key component of the monitoring phase of multicenter studies. One of the most popular techniques in this field is the Poisson-Gamma recruitment model,…
Characterizing patterns in police stops by race in Minneapolis from 2016-2021
Tuviere Onookome-Okome, Jonah Gorondensky, Eric Rose +3
The murder of George Floyd centered Minneapolis, Minnesota, in conversations on racial injustice in the US. We leverage open data from the Minneapolis Police Department to analyze…
Variable Selection for Individualized Treatment Rules with Discrete Outcomes
Zeyu Bian, Erica EM Moodie, Susan M Shortreed +2
An individualized treatment rule (ITR) is a decision rule that aims to improve individual patients health outcomes by recommending optimal treatments according to patients specific…
Causal inference: critical developments, past and future
Erica EM Moodie, David A Stephens
Causality is a subject of philosophical debate and a central scientific issue with a long history. In the statistical domain, the study of cause and effect based on the notion of `…
Privacy-preserving estimation of an optimal individualized treatment rule : A case study in maximizing time to severe depression-related outcomes
Erica EM Moodie, Janie Coulombe, Coraline Danieli +2
Estimating individualized treatment rules - particularly in the context of right-censored outcomes - is challenging because the treatment effect heterogeneity of interest is often…
Causal inference under mis-specification: adjustment based on the propensity score
David A. Stephens, Widemberg S. Nobre, Erica E. M. Moodie +1
We study Bayesian approaches to causal inference via propensity score regression. Much of the Bayesian literature on propensity score methods have relied on approaches that cannot…
Double robust estimation of partially adaptive treatment strategies
Denis Talbot, Erica EM Moodie, Caroline Diorio
Precision medicine aims to tailor treatment decisions according to patients' characteristics. G-estimation and dynamic weighted ordinary least squares (dWOLS) are double robust sta…
Variable Selection in Regression-based Estimation of Dynamic Treatment Regimes
Zeyu Bian, Erica EM Moodie, Susan M Shortreed +1
Dynamic treatment regimes (DTRs) consist of a sequence of decision rules, one per stage of intervention, that finds effective treatments for individual patients according to patien…
General Regression Methods for Respondent-Driven Sampling Data
Mamadou Yauck, Erica E. M. Moodie, Herak Apelian +5
Respondent-Driven Sampling (RDS) is a variant of link-tracing sampling techniques that aim to recruit hard-to-reach populations by leveraging individuals' social relationships. As…
Neighbourhood Bootstrap for Respondent-Driven Sampling
Mamadou Yauck, Erica E. M. Moodie, Herak Apelian +5
Respondent-Driven Sampling (RDS) is a form of link-tracing sampling, a sampling technique used for `hard-to-reach' populations that aims to leverage individuals' social relationshi…
The role of exchangeability in causal inference
Olli Saarela, David A. Stephens, Erica E. M. Moodie
Though the notion of exchangeability has been discussed in the causal inference literature under various guises, it has rarely taken its original meaning as a symmetry property of…
Sampling from Networks: Respondent-Driven Sampling
Mamadou Yauck, Erica E. M. Moodie, Herak Apelian +6
Respondent-Driven Sampling (RDS) is a variant of link-tracing, a sampling technique for surveying hard-to-reach communities that takes advantage of community members' social networ…
Formulating causal questions and principled statistical answers
Els Goetghebeur, Saskia le Cessie, Bianca De Stavola +2
Although review papers on causal inference methods are now available, there is a lack of introductory overviews on what they can render and on the guiding criteria for choosing one…
Generalized G-estimation and Model Selection
M. P. Wallace, E. E. M. Moodie, D. A. Stephens
Dynamic treatment regimes (DTRs) aim to formalize personalized medicine by tailoring treatment decisions to individual patient characteristics. G-estimation for DTR identification…