NorPEN Webinar Registration
Welcome to the registration for the NorPEN Webinar on November 11th 2020 2pm until 4 pm (CET).
The webinar will be conducted using ZOOM and meeting invitation will be sent to all registered.

Attendance is free of charge. Registration will close November 4th 2020.

November 11, 2020
14.00-14.50 An overview of G methods by Jessica Young*
14.50-15.00 10 minute break
15.00-16.00 Presentations from 5 junior researchers conducting studies on pharmacoepidemiology in Nordic data (TBA)

*This presentation is an introduction to G methods and teaser for the full NorPEN2021 pre-conference course on G-methods in relation to pharmacoepidemiology.

G-methods is a class of methods for estimating the causal effects of time-varying treatment strategies in longitudinal studies where time-varying confounders may be affected by past treatment. G-methods specifically aim to estimate Robins’s g-formula, a function of only measured study variables. Under assumptions that include no unmeasured confounding, the g-formula indexed by a particular time-varying treatment strategy equals the (counterfactual) outcome mean in the study population had all individuals adhered to that strategy. The g-formula is usually a high-dimensional function when the dimension of measured confounders is high and/or there are many follow-up times. Different g-methods (e.g. inverse probability weighting, parametric g-computation, targeted maximum likelihood estimation) constitute different estimation methods for this function of the longitudinal data. In this presentation, we will introduce counterfactual causal reasoning that motivates the g-formula as a target of statistical analysis and give a high-level overview of some different estimation methods.

Jessica Young, PhD
Assistant Professor and Biostatistician
Department of Population Medicine, Harvard Medical School
https://www.populationmedicine.org/JYoung

Her research focuses on the development and application of statistical methods for estimating policy and clinically relevant causal effects of time-varying treatment strategies on health outcomes in the face of complex time-varying confounding and selection bias, competing events and treatments that are challenging to measure.
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