52 citations · 63 across the 22 of their papers we have counts for
23 papers
Kernel weighted importance sampling for off-policy evaluation in contextual bandits
Joshua Spear, Matthieu Komorowski, Rebecca Pope +1
This article presents a novel estimator for performing off-policy evaluation using only offline data for contextual bandits. The proposed estimator, Kernel-WIS is demonstrated to b…
A Bayesian adaptive enrichment design using aggregate historical data to inform individualized treatment recommendations
Lara Maleyeff, Shirin Golchi, Erica E. M. Moodie
Adaptive enrichment trials aim to identify and recruit participants most likely to benefit from treatment based on evolving biomarker evidence, with the goal of informing individua…
Doubly-Robust Bayesian Estimation of Optimal Individualized Treatment Rules using Network Meta-Analysis
Augustine Wigle, Erica E. M. Moodie
An optimal individualized treatment rule (ITR) is a function that takes a patient's characteristics, such as demographics, biomarkers, and treatment history, and outputs a treatmen…
Personalized Treatment Hierarchies in Bayesian Network Meta-Analysis
Augustine Wigle, Erica E. M. Moodie
Network Meta-Analysis (NMA) is an increasingly popular evidence synthesis tool that can provide a ranking of competing treatments, also known as a treatment hierarchy. Treatment-Co…
Plasmode simulation for the evaluation of causal inference methods in homophilous social networks
Vanessa McNealis, Erica E. M. Moodie, Nema Dean
Typical simulation approaches for evaluating the performance of statistical methods on populations embedded in social networks may fail to capture important features of real-world…
Joint mixed-effects models for causal inference in clustered network-based observational studies
Vanessa McNealis, Erica E. M. Moodie, Nema Dean
Causal inference on populations embedded in social networks poses technical challenges, since the typical no interference assumption frequently does not hold. Existing methods deve…
Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention
Brian D. Williamson, Erica E. M. Moodie, Gregory E. Simon +2
Risk of suicide attempt varies over time. Understanding the importance of risk factors measured at a mental health visit can help clinicians evaluate future risk and provide approp…
Bayesian Optimization for Personalized Dose-Finding Trials with Combination Therapies
James Willard, Shirin Golchi, Erica E. M. Moodie +2
Identification of optimal dose combinations in early phase dose-finding trials is challenging, due to the trade-off between precisely estimating the many parameters required to fle…
Bayesian inference for optimal dynamic treatment regimes in practice
Daniel Rodriguez Duque, Erica E. M. Moodie, David A. Stephens
In this work, we examine recently developed methods for Bayesian inference of optimal dynamic treatment regimes (DTRs). DTRs are a set of treatment decision rules aimed at tailorin…
Revisiting the Effects of Maternal Education on Adolescents' Academic Performance: Doubly Robust Estimation in a Network-Based Observational Study
Vanessa McNealis, Erica E. M. Moodie, Nema Dean
In many contexts, particularly when study subjects are adolescents, peer effects can invalidate typical statistical requirements in the data. For instance, it is plausible that a s…
Dynamic Treatment Regimes using Bayesian Additive Regression Trees for Censored Outcomes
Xiao Li, Brent R Logan, S M Ferdous Hossain +1
To achieve the goal of providing the best possible care to each patient, physicians need to customize treatments for patients with the same diagnosis, especially when treating dise…
Estimating Individualized Treatment Rules in Longitudinal Studies with Covariate-Driven Observation Times
Janie Coulombe, Erica E. M. Moodie, Susan M. Shortreed +1
The sequential treatment decisions made by physicians to treat chronic diseases are formalized in the statistical literature as dynamic treatment regimes. To date, methods for dyna…
Using Pilot Data to Size Observational Studies for the Estimation of Dynamic Treatment Regimes
Eric J. Rose, Erica E. M. Moodie, Susan Shortreed
There has been significant attention given to developing data-driven methods for tailoring patient care based on individual patient characteristics. Dynamic treatment regimes forma…
Monte Carlo Sensitivity Analysis for Unmeasured Confounding in Dynamic Treatment Regimes
Eric J. Rose, Erica E. M. Moodie, Susan Shortreed
Data-driven methods for personalizing treatment assignment have garnered much attention from clinicians and researchers. Dynamic treatment regimes formalize this through a sequence…
New designs for Bayesian adaptive cluster-randomized trials
Junwei Shen, Shirin Golchi, Erica E. M. Moodie +1
Adaptive approaches, allowing for more flexible trial design, have been proposed for individually randomized trials to save time or reduce sample size. However, adaptive designs fo…
Estimating the Marginal Effect of a Continuous Exposure on an Ordinal Outcome using Data Subject to Covariate-Driven Treatment and Visit Processes
Janie Coulombe, Erica E M Moodie, Robert W Platt
In the statistical literature, a number of methods have been proposed to ensure valid inference about marginal effects of variables on a longitudinal outcome in settings with irreg…
Preserving data privacy when using multi-site data to estimate individualized treatment rules
Coraline Danieli, Erica Moodie
Precision medicine is a rapidly expanding area of health research wherein patient level information is used to inform treatment decisions. A statistical framework helps to formaliz…
Causal Inference for Quantile Treatment Effects
Shuo Sun, Erica E. M. Moodie, Johanna G. Nešlehová
Analyses of environmental phenomena often are concerned with understanding unlikely events such as floods, heatwaves, droughts or high concentrations of pollutants. Yet the majorit…
Evaluating the Use of Generalized Dynamic Weighted Ordinary Least Squares for Individualized HIV Treatment Strategies
Larry Dong, Erica E. M. Moodie, Laura Villain +1
Dynamic treatment regimes (DTR) are a statistical paradigm in precision medicine which aim to optimize patient outcomes by individualizing treatments. At its simplest, a DTR may re…
Bayesian Sample Size Calculations for SMART Studies
Armando Turchetta, Erica E. M. Moodie, David A. Stephens +1
In the management of most chronic conditions characterized by the lack of universally effective treatments, adaptive treatment strategies (ATSs) have been growing in popularity as…
Estimation of the marginal effect of antidepressants on body mass index under confounding and endogenous covariate-driven monitoring times
Janie Coulombe, Erica E. M. Moodie, Robert W. Platt +1
In studying the marginal effect of antidepressants on body mass index using electronic health records data, we face several challenges. Patients' characteristics can affect the exp…
Estimation of Optimal Dynamic Treatment Regimes using Gaussian Process Emulation
Daniel Rodriguez Duque, David A. Stephens, Erica E. M. Moodie
In precision medicine, identifying optimal sequences of decision rules, termed dynamic treatment regimes (DTRs), is an important undertaking. One approach investigators may take to…
Effect of breastfeeding on gastrointestinal infection in infants: A targeted maximum likelihood approach for clustered longitudinal data
Mireille E. Schnitzer, Mark J. van der Laan, Erica E. M. Moodie +1
The PROmotion of Breastfeeding Intervention Trial (PROBIT) cluster-randomized a program encouraging breastfeeding to new mothers in hospital centers. The original studies indicated…