Upcoming Events

SMDM Decision Psychology Pilot Idea Research Tournament: Session 3

14 September 2026

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!

Preference Measurement Journal Club

15 September 2026

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.

Highlights from the R-HTA book

18 September 2026

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.

SMDM Webinar: Calibrating Markov Models to Clinical Survival Data

08 October 2026

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.

Causal Machine Learning for Health Economics and Decision Making

19 November 2026

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.