Erica EM Moodie

Professor

McGill University

Canada

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Erica EM Moodie is a Professor at McGill University in Canada. Her research primarily focuses on clinical trials, biostatistics, and health services utilization, with recent articles examining topics such as the effects of bisphenol concentrations in breast milk and methodologies for pragmatic estimation strategies. She also explores the application of evidence-based interventions in health care settings, particularly for patients facing chronic conditions.

Erica EM Moodie

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Articles (20)

Sparse 2-stage Bayesian meta-analysis for individualized treatments

Translating Evidence‐Based Self‐Management Interventions Using a Stepped‐Care Approach for Patients With Cancer and Their Caregivers: A Pilot Sequential Multiple Assignment Randomized Trial Design

Breast milk bisphenol concentrations in Canada and South Africa and associations with body size among South African infants

A Framework for Generating Realistic Synthetic Tabular Data in a Randomized Controlled Trial Setting

SMART Designs: Bridging the Gap Between Clinical Trials and Practice in Infectious Diseases

A Simple Diagnostic for the Positivity Assumption for Continuous Exposures

Natural Effects in the Presence of an Intermediate Confounder: Evaluation of Pragmatic Estimation Strategies With an Emphasis on the Relationship Between Natural and Interventional Effects

Effect of depressive symptoms on health services utilization in the HIV and hepatitis C co-infected population in Canada

Commentary: Mendelian randomization for causal inference

A retrospective and prospective study of biostatistics in Canada

Double robust estimation of optimal partially adaptive treatment strategies: An application to breast cancer treatment using hormonal therapy

The impact of directly observed therapy on the efficacy of Tuberculosis treatment: a Bayesian multilevel approach

A time‐dependent Poisson‐Gamma model for recruitment forecasting in multicenter studies

Dynamic Treatment Regimes Using Bayesian Additive Regression Trees for Censored Outcomes

Bayesian sample size calculations for comparing two strategies in SMART studies

Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms

Causal Inference and Confounding: A Primer for Interpreting and Conducting Infectious Disease Research

Semiparametric Bayesian inference for optimal dynamic treatment regimes via dynamic marginal structural models

Monte Carlo sensitivity analysis for unmeasured confounding in dynamic treatment regimes

Variable selection in regression‐based estimation of dynamic treatment regimes

Collaborators (10)

Denis Talbot

Andréa Maria Laizner

Shirin Golchi

Saskia Le Cessie

David Stephens

Sahir Bhatnagar

Sylvie Lambert

Alexandra M. Schmidt

Junwei Shen

Sydney Wasserman

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