5 papers · 1 filter
Valid post-selection inference for penalized G-estimation
Ajmery Jaman, Ashkan Ertefaie, Michèle Bally +3
Understanding treatment effect heterogeneity is important for decision making in medical and clinical practices, or handling various engineering and marketing challenges. When deal…
Is checking for sequential positivity violations getting you down? Try sPoRT!
Arthur Chatton, Michael Schomaker, Miguel-Angel Luque-Fernandez +2
Background: Sequential positivity is often a necessary assumption for drawing causal inferences, such as through marginal structural modeling. Unfortunately, verification of this a…
Penalized G-estimation for effect modifier selection in a structural nested mean model for repeated outcomes
Ajmery Jaman, Guanbo Wang, Ashkan Ertefaie +4
Effect modification occurs when the impact of the treatment on an outcome varies based on the levels of other covariates known as effect modifiers. Modeling these effect difference…
What if we had built a prediction model with a survival super learner instead of a Cox model 10 years ago?
Arthur Chatton, Ãmilie Pilote, Kevin Assob Feugo +3
Objective: This study sought to compare the drop in predictive performance over time according to the modeling approach (regression versus machine learning) used to build a kidney…
Personalised dynamic super learning: an application in predicting hemodiafiltration convection volumes
Arthur Chatton, Michèle Bally, Renée Lévesque +3
Obtaining continuously updated predictions is a major challenge for personalised medicine. Leveraging combinations of parametric regressions and machine learning approaches, the pe…