3 citations · 6 across the 7 of their papers we have counts for
7 papers
Penalized generalized linear mixed models for longitudinal outcomes in genetic association studies
Julien St-Pierre, Sahir Rai Bhatnagar, Massimiliano Orri +3
This work is motivated by analyses of longitudinal data collected from participants in the Quebec Longitudinal Study of Child Development (QLSCD) and the Quebec Newborn Twin Study…
Case-Base Neural Networks: survival analysis with time-varying, higher-order interactions
Jesse Islam, Maxime Turgeon, Robert Sladek +1
In the context of survival analysis, data-driven neural network-based methods have been developed to model complex covariate effects. While these methods may provide better predict…
Efficient Penalized Generalized Linear Mixed Models for Variable Selection and Genetic Risk Prediction in High-Dimensional Data
Julien St-Pierre, Karim Oualkacha, Sahir Rai Bhatnagar
Sparse regularized regression methods are now widely used in genome-wide association studies (GWAS) to address the multiple testing burden that limits discovery of potentially impo…
Variable Selection for Individualized Treatment Rules with Discrete Outcomes
Zeyu Bian, Erica EM Moodie, Susan M Shortreed +2
An individualized treatment rule (ITR) is a decision rule that aims to improve individual patients health outcomes by recommending optimal treatments according to patients specific…
Variable Selection in Regression-based Estimation of Dynamic Treatment Regimes
Zeyu Bian, Erica EM Moodie, Susan M Shortreed +1
Dynamic treatment regimes (DTRs) consist of a sequence of decision rules, one per stage of intervention, that finds effective treatments for individual patients according to patien…
casebase: An Alternative Framework For Survival Analysis and Comparison of Event Rates
Sahir Rai Bhatnagar, Maxime Turgeon, Jesse Islam +2
In epidemiological studies of time-to-event data, a quantity of interest to the clinician and the patient is the risk of an event given a covariate profile. However, methods relyin…
Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex Penalties
Kai Yang, Masoud Asgharian, Sahir Bhatnagar
Nesterov's accelerated gradient (AG) is a popular technique to optimize objective functions comprising two components: a convex loss and a penalty function. While AG methods perfor…