Causal inferences from complex observational and randomized studies with time-varying exposures or treatments.
published: Oct. 6, 2014, recorded: December 2013, views: 36
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
I review identification and estimation of direct and indirect effects of time-varying treatments or actions. I describe the relationship between a number of modeling approaches: marginal structural models, the parametric g-formula, the iterated conditional expectation g-formula, direct effect structural nested models, and nested Markov models. I describe the strengths and weaknesses of each modeling approach and give examples of their application in medicine and public health. Finally I show how SWIGs (single world intervention graphs) can be used to effortlessly translate between the counterfactual and graphical approaches to causation.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !