Society for Medical Decision Making

07.00 PDT | 10.00 EDT | 12.00 UTC | 16.00 CEST
1.5 hours

The aim of this webinar is to show how causal inference methods and decision-analytic modeling can be combined to correct for bias in empirical studies.

The webinar consists of two parts, followed by a Q&A:

• In Part 1, we give a brief introduction to the toolbox of causal inference for observational studies and randomized controlled trials, including causal graphs, target trial emulation, g-methods, and the use of decision-analytic modeling to address causal research questions.

• In Part 2, we demonstrate the application of causal modeling to correct for treatment switching bias in a randomized clinical trial of ovarian cancer treatment.

This webinar complements a methodological research paper published in Medical Decision Making:

Kuehne F, Rochau U, Paracha N, Yeh JM, Sabate E, Siebert U. Estimating Treatment-Switching Bias in a Randomized Clinical Trial of Ovarian Cancer Treatment: Combining Causal Inference with Decision-Analytic Modeling.
Medical Decision Making. 2022;42(2):194-207. doi: 10.1177/0272989X211026288.

Presented by:

Felicitas Kune, MSc., Dr.phil
UMT TIROL - University for Health Sciences and Technology, Austria
Pfizer Pharma GmbH, Germany

Uwe Siebert, MD, MPH, MSc, ScD
UMIT TIROL - University for Health Sciences and Technology, Austria
Harvard Chan School of Public Health, USA