This course uses a simulated, but realistic, patient-level dataset to illustrate the key concepts, which are like building blocks introduced with increasing sophistication. Ultimately the course aims to show students how to analyse these kinds of data to estimate within-study quantities (e.g. differential mean costs) or to derive key input parameters to populate a cost-effectiveness model to inform HTA decisions.

Course Description

This three-day advanced course focuses on the use of statistical methods for the analysis of individual patient-level cost, effects (e.g. survival and health-related quality of life) and other type of data used in cost-effectiveness analysis for HTA. It is intended for people who wish to learn how to apply (and interpret the results of) more advanced techniques for the analysis of data collected alongside both experimental (e.g. RCTs) and observational (sometimes referred to as “real-world”) studies, where the objective is to estimate within-study quantities (e.g. differential mean costs) or to derive key input parameters to populate economic evaluation models for HTA. The course includes a mixture of taught modules and practical exercises.