Date: Friday, March 7, 2025
Time: 12:00–1:00 p.m. Atlantic time
Location: On Zoom, live from Memorial University of Newfoundland
This talk will be presented by Shenita Pramij, a PhD student in the Department of Mathematics and Statistics at Memorial University of Newfoundland. It’s the fifth event in the Atlantic Canada Data Science Tour, a hybrid seminar series organized by CANSSI Atlantic and geared toward upper-level undergraduates in statistics or computer science programs. The host will be Yildiz Yilmaz, Associate Professor of Statistics in the Department of Mathematics and Statistics at Memorial University.
This event will be online only, live from Memorial University of Newfoundland. We invite you to join us! (We’ll send you the Zoom link when you register.)
Please note that this talk will begin at noon Atlantic time (not Newfoundland time).
Inferring the direct effects of exposure in recurrent event processes, while accounting for mediating factors, is crucial, yet conventional approaches face significant limitations in the presence of complex causal relationships. We introduce two methods to address these challenges. We first explore a two-stage sequential G-estimation method to estimate the controlled direct effect of a randomly assigned exposure, while accounting for potential mediators and confounders, using intensity-based models of recurrent event processes. We also introduce a novel one-stage estimation method based on the estimating equations framework, leveraging the sequential G-estimation principle. We demonstrate that both methods yield unbiased controlled direct effect estimates. The one-stage method also enables the analytical derivation of an estimator for the standard error of the direct effect estimator. We illustrate our approach using a hospital readmission dataset of colorectal cancer patients to estimate the controlled direct effect of sex differences on hospital readmission.
Shenita Pramij is a PhD student in Statistics at Memorial University of Newfoundland. Her research focuses on mediation analysis, with a particular emphasis on estimating controlled direct effects in recurrent event processes. She has broad interests in modelling complex processes with applications in healthcare and public policy, particularly in using statistical methods to analyze disease dynamics and assess intervention effects.
Beyond her doctoral research, Shenita has extensive experience in the public sector as a compliance researcher, where she applies causal inference techniques to evaluate the impact of policies and interventions on compliance. Her work aims to enhance decision-making in public policy and inform targeted interventions.
Shenita holds a Master of Science in Statistics and a Bachelor of Science in Pure Mathematics from Memorial University.