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The SMDM Career Development Committee invites members, particularly trainees and early-career researchers, to join a panel discussion on navigating the academic job market. Hear from recent candidates and a faculty member with search committee experience as they share practical advice on preparing application materials, interviewing, negotiating offers, and successfully pursuing postdoctoral and faculty positions. The session will conclude with a live Q&A, providing attendees the opportunity to ask questions and gain valuable career insights.
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The Decision Psychology Special Interest Group (SIG) is pleased to announce the SMDM Decision Psychology Pilot Research Idea Tournament. Please join us at any of the following sessions listed at the bottom of the description. All are welcome!
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Tina Cheng, PhD Candidate, will lead a discussion of the paper, What Is the Consensus Value of Patients’ Treatment-Risk Tolerance? Assessing a Stated-Preference Evidence Base for Inflammatory Bowel Disease by Johnson et al.
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This course will present highlights from the recent “R for Health Technology Assessment (HTA)” edited textbook. These will take the form of short tutorials explaining how to accomplish key analyses relevant to HTA using the R programming language.
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Building accurate Markov models is challenging because they cannot easily be matched up with clinical survival data. Partitioned Survival models are built directly on survival curves that can be fitted to clinical data through regression analysis. However, Partitioned Survival models are limited to forward progression, and they do not support critical events beyond implicit progression and death. Markov models are instead built on distinct events including, but not limited to, progression and death. However, Markov models cannot be directly fitted to clinical survival data because disease progression results from a combination of event risks applied over time. For Markov models, curve-fitting calibration techniques can refine event probabilities to match multi-cause disease progression to clinical survival data.
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This course introduces methods for combining machine learning (ML) and causal inference to improve health economic evaluations and decision-making. Participants will learn how ML can be used for prediction, estimating treatment effects, and designing stratified treatment policies. The course will also demonstrate practical implementation of these methods in R, with accompanying code provided.
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6-9 June, 2027: Join us in Toronto for SMDM’s 49th Annual Meeting as we explore universal, sustainable, patient-centred healthcare through better decision making. The meeting will bring together 300-500 scholars, clinicians, public health policy makers, trainees, students, and early career researchers dedicated to advancing medical decision making. It will feature short courses, oral sessions, symposia, clinician focused sessions, poster presentations, and the prestigious Lee B. Lusted Student Prize Competition. Expand your professional network by connecting with colleagues throughout the event!
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Save the Date – SMDM’s 50th Annual Meeting Mark your calendars for a milestone celebration! Join us 11–14 June, 2028, in Salt Lake City, Utah, as we celebrate SMDM’s 50th Annual Meeting. For five decades, the Society for Medical Decision Making has brought together researchers, clinicians, policymakers, and decision scientists from around the world to advance the science and practice of medical decision making. The 50th Annual Meeting will honor this remarkable legacy while looking ahead to the future of our field through outstanding scientific programming, meaningful collaboration, and unforgettable networking opportunities. We look forward to celebrating this historic milestone with you in Salt Lake City. More details, including the theme, program, and registration information, will be shared.