activity
20212024
most citedEstimating the Marginal Effect of a Continuous Exposure on an Ordinal Outcome using Data Subject to Covariate-Driven Treatment and Visit Processes

6 citations · 6 across the 8 of their papers we have counts for

collaborators

8 papers

stat.CO2024

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…

stat.ME2024

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…

stat.ME2024

Estimating hidden population size from a single respondent-driven sampling survey

Mamadou Yauck, Erica EM Moodie, Alain Fourmigue +4

This work is concerned with the estimation of hard-to-reach population sizes using a single respondent-driven sampling (RDS) survey, a variant of chain-referral sampling that lever…

stat.ME2023

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…

stat.ME2023

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…

stat.ME2023

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,…