New designs for Bayesian adaptive cluster randomized trials for an individualized clinical support tool with capacity to support distance follow up and treatment of depression

Depression is a common and often devastating illness that contributes to suffering for patients and families and is also the number one cause of disability globally. Many patients do not respond to their
first trial of treatment, and managing depression according to best practices can be difficult for clinicians. Using the power of machine learning, a new tool has been developed that is intended to help match
patients to treatments using a simple questionnaire and to assist clinicians in improving the quality of depression treatment. It is now time to test this tool in a clinical trial, and this project aims to help ensure
that this trial is designed to take into account new challenges and questions that come with this kind of tool, and that the analysis of the results of the study is rigorous and provide meaningful answers about
the effect of the tool on patient outcomes.

Faculty Supervisor:

Erica Moodie;Shirin Golchi

Student:

Junwei Shen

Partner:

Aifred Health Inc

Discipline:

Epidemiology / Public health and policy

Sector:

University:

McGill University

Program: