Zobrazeno 1 - 10
of 631
pro vyhledávání: '"Talbot Denis"'
Autor:
Talbot Denis, Beaudoin Claudia
Publikováno v:
Journal of Causal Inference, Vol 10, Iss 1, Pp 335-371 (2022)
Analysts often use data-driven approaches to supplement their knowledge when selecting covariates for effect estimation. Multiple variable selection procedures for causal effect estimation have been devised in recent years, but additional development
Externí odkaz:
https://doaj.org/article/a571bce4e06846d59e4cb8eb063a45a6
Autor:
Diop, Awa, Sirois, Caroline, Guertin, Jason R., Schnitzer, Mireille E., Brophy, James M., Talbot, Denis
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 technique, and marginal structural models (MSMs) called LCGA-MSM. LCGA summ
Externí odkaz:
http://arxiv.org/abs/2410.19682
Autor:
Schnitzer, Mireille E, Talbot, Denis, Liu, Yan, Berger, David, Wang, Guanbo, O'Loughlin, Jennifer, Sylvestre, Marie-Pierre, Ertefaie, Ashkan
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 directly applicable to time-varying scenarios. We propose a novel two-s
Externí odkaz:
http://arxiv.org/abs/2410.08283
Autor:
Adenyo, David, Schnitzer, Mireille E, Berger, David, Guertin, Jason R, Candas, Bernard, Talbot, Denis
Criteria for identifying optimal adjustment sets (i.e., yielding a consistent estimator with minimal asymptotic variance) for estimating average treatment effects in parametric and nonparametric models have recently been established. In a single trea
Externí odkaz:
http://arxiv.org/abs/2410.01000
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 models are often estimated using inverse probability of treatment weig
Externí odkaz:
http://arxiv.org/abs/2403.08577
While 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, it is susceptible to selection bias due to outcome-dependent sampling
Externí odkaz:
http://arxiv.org/abs/2310.04578
Publikováno v:
Journal of Causal Inference, Vol 3, Iss 2, Pp 207-236 (2015)
Estimating causal exposure effects in observational studies ideally requires the analyst to have a vast knowledge of the domain of application. Investigators often bypass difficulties related to the identification and selection of confounders through
Externí odkaz:
https://doaj.org/article/72b3f38203b849f58d18090d5a8c3108
Autor:
Talbot, Denis, Mésidor, Miceline, Trenou, Kossi Clément, Lavigne-Robichaud, Mathilde, Trudel, Xavier, Eslami, Aida
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 population been exposed to a given exposure. This quantity thus plays a crucia
Externí odkaz:
http://arxiv.org/abs/2212.09538
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 methods have been criticized especially because of the data-driven model
Externí odkaz:
http://arxiv.org/abs/2205.07631
Publikováno v:
In Value in Health October 2024 27(10):1393-1399