Department of Population Health Sciences

THIS EVENT HAS BEEN POSTPONED UNTIL 2021. REGISTRATION WILL BE RE-OPENED AS DETAILS BECOME AVAILABLE.

The Department of Population Health Sciences is proud to welcome back Miguel Hernan, MD, ScM, DrPH, Harvard T.H. Chan School of Public Medicine for a two day workshop.

 **REGISTRATION IS REQUIRED TO ATTEND THIS EVENT

Miguel Hernan Workshop May 13th -14th, 2020

For health researchers or other data scientists who will use longitudinal observational studies to estimate causal effects as part of their current or future professional career. Learn how to determine “what works” using data from longitudinal observational with time-varying treatments and confounding.

Causal inference from observational data is a key task of epidemiology and other allied disciplines such as health economics and health services research. Commonly used statistical methods estimate association measures that cannot always be endowed with causal interpretations, even when all measured confounders are included in the analysis. In contrast, a causally explicit approach formally defines causal effects, identifies the conditions required to estimate causal effects without bias, and uses analytical methods that, under those conditions, provide estimates that can be endowed with a causal interpretation. This workshop presents such framework for causal inference from observational data and recent methodological developments, with a special emphasis on complex longitudinal data with time-varying treatments and confounding. The application of these methods will be illustrated using data from a synthetic HIV cohort study. The course is aimed at epidemiologists, statisticians, and other researchers who work with longitudinal observational data.

By the end of this Workshop participants will have

• An understanding of confounding and selection bias

• An understanding of the role and potential of different methodological approaches to overcome these problems, including inverse probability weighting, marginal structural models and the G-formula

• Done practical data analyses implementing these methods

Schedule:

Wednesday: 

9:00 am - 1:00 pm - Health Sciences Education Building, Room 1730

Thursday: 

9:00 am - 1:00 pm - Health Sciences Education Building, Room 1730