Publications (12)
Marginal structural models with Latent Class Growth Modeling of Treatment Trajectories
Awa Diop, Caroline Sirois, Jason Robert Guertin +1
In a real-life setting, little is known regarding the effectiveness of statins for primary prevention among older adults, and analysis of observational data can add crucial informa…
trajmsm: An R package for Trajectory Analysis and Causal Modeling
Awa Diop, Caroline Sirois, Jason R. Guertin +3
The R package trajmsm provides functions designed to simplify the estimation of the parameters of a model combining latent class growth analysis (LCGA), a trajectory analysis techn…
A bootstrap approach for validating the number of groups identified by latent class growth models
Miceline Mésidor, Caroline Sirois, Marc Simard +1
The use of longitudinal finite mixture models such as group-based trajectory modeling has seen a sharp increase during the last decades in the medical literature. However, these me…
An Alternative Perspective on the Robust Poisson Method for Estimating Risk or Prevalence Ratios
Denis Talbot, Miceline Mésidor, Yohann Chiu +2
The robust Poisson method is becoming increasingly popular when estimating the association of exposures with a binary outcome. Unlike the logistic regression model, the robust Pois…
A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the Study of Osteoporotic Fractures
Denis Talbot, Claudia Beaudoin
Analysts often use data-driven approaches to supplement their substantive knowledge when selecting covariates for causal effect estimation. Multiple variable selection procedures t…
Efficient adjustment sets for time-dependent treatment effect estimation in nonparametric causal graphical model
David Adenyo, Mireille E Schnitzer, David Berger +2
Criteria for identifying optimal adjustment sets yielding consistent estimation with minimal asymptotic variance of average treatment effects in parametric and nonparametric models…
A Double Machine Learning Approach for the Evaluation of COVID-19 Vaccine Effectiveness under the Test-Negative Design: Analysis of Québec Administrative Data
Cong Jiang, Denis Talbot, Sara Carazo +1
The test-negative design (TND), which is routinely used for monitoring seasonal flu vaccine effectiveness (VE), has recently become integral to COVID-19 vaccine surveillance, notab…
Evaluation and comparison of covariate balance metrics in studies with time-dependent confounding
David Adenyo, Jason R. Guertin, Bernard Candas +2
Marginal structural models have been increasingly used by analysts in recent years to account for confounding bias in studies with time-varying treatments. The parameters of these…
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…
Adaptive sparsening and smoothing of the treatment model for longitudinal causal inference using outcome-adaptive LASSO and marginal fused LASSO
Mireille E Schnitzer, Denis Talbot, Yan Liu +5
Causal variable selection in time-varying treatment settings is challenging due to evolving confounding effects. Existing methods mainly focus on time-fixed exposures and are not d…
The positivity assumption in causal mediation analyses? Checked!
Arthur Chatton, Geneviève Lefebvre, Mireille E. Schnitzer +1
Causal mediation analyses are increasingly used in psychological sciences. Among the required assumptions, positivity is unfortunately seldom mentioned, likely due to the lack of t…
Estimation of the attributable fraction for time to event outcomes using an inverse probability of exposure weighted Kaplan-Meier estimator
Denis Talbot, Miceline Mésidor, Kossi Clément Trenou +3
Population attributable fractions aim to quantify the proportion of the cases of an outcome (for example, a disease) that would have been avoided had no individuals in the populati…