2

Personalised dynamic super learning: an application in predicting hemodiafiltration convection volumes

Obtaining continuously updated predictions is a major challenge for personalised medicine. Leveraging combinations of parametric …

Arthur Chatton, Michèle Bally, Renée Lévesque, Ivana Malenica, Robert Platt, Mireille Schnitzer

The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization

Recent developments in the causal-inference literature have renewed psychologists\’ interest in how to improve causal conclusions based …

Arthur Chatton, Julia Rohrer

Identification of in-sample positivity violations using regression trees: The PoRT algorithm

Gabriel Danelian, Yohann Foucher, Maxime Léger, Florent Le Borgne, Arthur Chatton

Causal inference in case of near-violation of positivity: comparison of methods

Maxime Léger, Arthur Chatton, Florent Le Borgne, Romain Pirracchio, Sigismond Lasocki, Yohann Foucher

Causal and Associational Language in Observational Health Research: A Systematic Evaluation

We estimated the degree to which language used in the high-profile medical/public health/epidemiology literature implied causality …

Noah A. Haber, Sarah E. Wieten, Julia M. Rohrer, Onyebuchi A. Arah, Peter W. G. Tennant, Elizabeth A. Stuart, Eleanor J. Murray, Sophie Pilleron, Sze Tung Lam, Emily Riederer, Sarah Jane Howcutt, Alison E. Simmons, Clémence Leyrat, Philipp Schoenegger, Anna Booman, Mi-Suk Kang Dufour, Ashley L. O’Donoghue, Rebekah Baglini, Stefanie Do, Mari De La Rosa Takashima, Thomas Rhys Evans, Daloha Rodriguez-Molina, Taym M. Alsalti, Daniel J. Dunleavy, Gideon Meyerowitz-Katz, Alberto Antonietti, Jose A. Calvache, Mark J. Kelson, Meg G. Salvia, Camila Olarte Parra, Saman Khalatbari-Soltani, Taylor McLinden, Arthur Chatton, Jessie Seiler, Andreea Steriu, Talal S. Alshihayb, Sarah E. Twardowski, Julia Dabravolskaj, Eric Au, Rachel A. Hoopsick, Shashank Suresh, Nicholas Judd, Sebastián Peña, Cathrine Axfors, Palwasha Khan, Ariadne E. Rivera Aguirre, Nnaemeka U. Odo, Ian Schmid, Matthew P. Fox

G-computation and doubly robust standardisation for continuous-time data: A comparison with inverse probability weighting

In time-to-event settings, g-computation and doubly robust estimators are based on discrete-time data. However, many biological …

Arthur Chatton, Florent Le Borgne, Clémence Leyrat, Yohann Foucher

Donor HLA Class 1 Evolutionary Divergence Is a Major Predictor of Liver Allograft Rejection

Cyrille Féray, Jean-Luc Taupin, Mylène Sebagh, Vincent Allain, Zeynep Demir, Marc-Antoine Allard, Christophe Desterke, Audrey Coilly, Faouzi Saliba, Eric Vibert, Daniel Azoulay, Catherine Guettier, Arthur Chatton, Dominique Debray, Sophie Caillat-Zucman, Didier Samuel

SMILE: a predictive model for Scoring the severity of relapses in MultIple scLErosis

In relapsing–remitting multiple sclerosis (RRMS), relapse severity and residual disability are difficult to predict. Nevertheless, this …

F. Lejeune, Arthur Chatton, D.-A. Laplaud, E. Le Page, S. Wiertlewski, G. Edan, A. Kerbrat, D. Veillard, S. Hamonic, N. Jousset, F. Le Frère, J.-C. Ouallet, B. Brochet, A. Ruet, Y. Foucher, Laure Michel

G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

In clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. …

Florent Le Borgne, Arthur Chatton, Maxime Léger, Rémi Lenain, Yohann Foucher

G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study

Controlling for confounding bias is crucial in causal inference. Distinct methods are currently employed to mitigate the effects of …

Arthur Chatton, Florent Le Borgne, Clémence Leyrat, Florence Gillaizeau, Chloé Rousseau, Laetitia Barbin, David Laplaud, Maxime Léger, Bruno Giraudeau, Yohann Foucher