About this Event
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.
Faculty
- Gianluca Baio, Bsc, MSc, PhD, Professor of Statistics and Health Economics, Department of Statistical Science, University College London (UK)
- Petros Pechlivanoglou, PhD, Senior Scientist, Hospital for Sick Children (Canada)
- Howard Thom, BA, MSc, PhD, Associate Professor in Health Economics, Health Economics and Health Policy, University of Bristol (UK)
Course Overview
Professor Baio will open with a session on missing data methods in R, illustrated with a case study in weight-loss interventions for obese adults. He will present frequentist and Bayesian techniques for multiple imputation and explain how to handle missingness in economic evaluations. Professor Pechlivanoglou will lead a session on survival analysis for HTA, focusing on fitting and comparing parametric time-to-event models in R. The session will cover survival extrapolation beyond observed follow-up, assessment of model fit and clinical plausibility, and generation of survival inputs for decision models. Dr Thom will conclude with a session on continuous-time multistate models, focusing on semi-Markov models in oncology using a colon cancer case study. This will illustrate the use of the msm package to estimate transition rates and the hesim package for forward simulation.