Post-Graduate Stories

Megan Jones Will Advance “Statistical Learning and Inference for Longitudinal Patient-Reported Outcomes in Cancer Trials”

As a 2026 Canstat-CANSSI Distinguished Postdoctoral Fellow, Megan Jones will take part in a comprehensive program that involves teaching, interdisciplinary or applied collaboration, professional development, and a research project involving the development of novel statistical methods for analyzing longitudinal ordinal questionnaire data, motivated by patient-reported outcomes (PROs) in oncology clinical trials. Megan will work under the supervision of Professor Wei Tu (Queen’s University) and Professor Yi Liu (York University).

Program: Canstat-CANSSI Distinguished Postdoctoral Fellowships
Region: National
Date: 2026–2028

Project Focus Areas

Megan’s work will focus on the development of novel statistical methods for analyzing longitudinal ordinal questionnaire data, motivated by patient-reported outcomes (PROs) in oncology clinical trials. Current approaches struggle with redundancy across items, unequal assessment schedules, and informative missingness, limiting their ability to capture patients’ experiences. The project proposes two advances: (i) sparse dynamic latent factor models to reduce redundancy and improve measurement sensitivity, and (ii) inference procedures that accommodate differential assessment frequency and missing not-at-random mechanisms through doubly robust estimation and joint modelling. Using rich datasets from the Canadian Cancer Trials Group (CCTG), these methods will generate interpretable endpoints that more efficiently capture patients’ voices, strengthening the role of PROs in cancer research and improving the patient-centredness of clinical trials.

This project is a blend of biostatistics, machine learning, oncology, and PROs, integrating methodological innovation with patient-centred cancer research. Megan will be embedded within the CCTG, Canada’s largest cooperative oncology network, gaining exposure to the design and analysis of international phase III trials. Through collaboration with oncologists, statisticians, quality-of-life researchers and patients, she will learn to frame statistical methods around clinically meaningful questions and patient experience.

In addition to participating in research, Megan will teach a graduate-level course in machine learning and will supervise and mentor graduate students and clinical fellows. She will have opportunities for career training in grant writing, manuscript preparation, and leadership.

Getting to Know Megan

Megan Jones is completing her PhD in Biostatistics at Vanderbilt University under the supervision of Professor Simon Vandekar. Her dissertation focuses on effect size estimation and inference, primarily applied to neuroimaging, with an additional focus on machine learning in predictive neuroimaging and improving estimation of predictive accuracy effect sizes via modern semiparametric methods. She has also taught graduate-level biostatistics courses and received a Distinguished Teaching Assistant Award at Vanderbilt.

Megan describes her passion as “biostatistical research that uses cutting-edge statistical methods to make a real-world impact in patients’ lives.” She expects to stay within academia and views the CANSSI Distinguished Postdoctoral Fellowship as an opportunity to “continue developing my skills for institutional research, including interdisciplinary collaborations, independent research, and securing funding.” She also plans to settle permanently in Canada and sees the program as a way to begin building a professional network with Canadian researchers.

I see myself staying in the academic sphere and would love to continue developing my skills for institutional research, including interdisciplinary collaborations, independent research, and securing funding.