My github page (JanieCoulombe Stat) contains more ressources on R codes and other material.
I am currently teaching a new graduate course in the Department of Mathematics and Statistics at UdeM, called Méthodes d’analyse biostatistique (STT6510) in which we go over the theory and application of methods for longitudinal data, survival analyses, and an introduction to causal inference.
Happy to report that my research is now funded by an NSERC discovery grant!
Links to two videos of presentations I’ve given recently:
For the Club Math at Université de Montréal on an Introduction to causal inference .
At CRCHU Quebec on causal diagrams .
I recently received the 2021-2022 Award for Excellence in Teaching Biostatistics at McGill University, an Award voted by the students in the Department of Epidemiology, Biostatistics and Occupational Health and given by the Epidemiology, Biostatistics and Occupational Health Student Society (EBOSS). I am super grateful for that! Thanks to EBOSS and Dr. Erica Moodie who shared course notes with me for the course BIOS602.
I am thrilled to announce that I will start a new position of Assistant Professor (Professeure Adjointe) at the Université de Montréal in June 2022, working in the Département de mathématiques et statistique.
I recently received the Pierre Robillard Award 2022, an Award given by the Statistical Society of Canada that “recognizes the best PhD thesis defended at a Canadian university in a given year and written in the fields covered by The Canadian Journal of Statistics” (https://ssc.ca/en/award/pierre-robillard-award).
In September 2021, I started a postdoctoral fellowship at McGill University working in collaboration with Professor Erica E. M. Moodie in the Department of Epidemiology, Biostatistics and Occupational Health.
In the Fall 2021, I was supposed to visit North Carolina State University (NCSU) for 6 months as a research scholar and to work in the Department of Statistics under the supervision of Professor Marie Davidian. Due to the covid19 pandemic, my collaboration turned to virtual. I am very pleased to have had this opportunity.
My doctoral research focused on causal inference, and the development (or transfer to the causal framework) of statistical methods that account for covariate-dependent monitoring times. In May 2021, I defended my doctoral thesis in Biostatistics at McGill University - I had the privilege to be supervised by Professor Erica EM Moodie and co-supervised by Professor Robert Platt during my doctoral studies.
In previous work, we considered imbalances in the data due to both confounding factors and outcome-dependent monitoring times when drawing causal inference on the marginal effect of an intervention on a continuous, longitudinal outcome. We also extended that work to the setting in which the covariate process affecting visit times is endogenous; endogeneity may create long-term dependencies between the outcome and the monitoring processes. In a more recent manuscript, we extended that work to continuous exposure and ordinal outcomes.
Other broad interests are in semiparametric theory (multiply robust estimators), optimal adaptive treatment strategies, and stochastic processes/multistate model theory for causal inference. With Prof. Shu Yang at North Carolina State University, we recently proposed a quadruply robust estimator that accounts for irregular visits and confounding. That estimator is flexible and has more opportunities to be unbiased when compared with the previously proposed doubly weighted estimator. It is also the most efficient among its class of semiparametric estimators.
Besides my doctoral and more methodological research, in 2019-2020, I have had the chance to be involved on a project in which we developed an optimal adaptive treatment strategy for patients suffering with depression. That rule was developed with the aim to choose between citalopram and fluoxetine (two commonly prescribed antidepressant drugs) based on patients’ characteristics. See the commentary by Drs Shiner and Watts and our response, as well as a link to the McGill Reporter short interview.
More details on previous publications can be found on my Google Scholar account. Please feel free to touch base if you would be interested in a collaboration!
I hold a Bachelor’s degree in Mathematics (Université de Montréal), a Master’s degree in Statistics (Université de Montréal), and a Doctorate in Biostatistics (McGill University). I completed a one-year postdoc at McGill University before obtaining a position at Université de Montréal.
Prior to my doctoral studies, I worked for two years as an analyst statistician in the McGill Pharmacoepidemiology Unit at the Lady Davis Research Institute, Jewish General Hospital, Montreal, CA, under the supervision of Dr. Samy Suissa. There, I worked extensively with observational data (from e.g. electronic health records data and administrative databases) and I learned a great deal through conducting complex data analyses and collaborating with world-renowned experts in pharmacoepidemiology. Following this, I have become more and more interested in research questions that are of interest to the scientific community of epidemiologists and pharmacoepidemiologists.