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: 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. Finally, we will discuss
questions from the audience in a Q&A session.

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. Med Decis Making. 2022;42(2):194-207. doi: 10.1177/0272989X211026288.