Estimating Treatment Effects Using Longitudinal Data Keywords: The availability and use of observational data---electronic medical records, claims databases, registries, etc.---is increasing in medical research. However, a valid estimation of the causal effects of treatment from observational data requires strong assumptions regarding confounding and other potential biases. Estimating the effects of time-varying treatments in the presence of time-varying confounding factors also requires the use of appropriate analytic methods. The goal of this workshop is to describe the implementation of several techniques for the estimation of causal treatment effects in longitudinal observational data. We will discuss the relative advantages and disadvantages of inverse probability weighting of marginal structural models and the parametric g-formula.
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Important Dates & Deadlines
- May 31, 2011
Registration Deadline for All Session Presenters - September 1, 2011
Poster Abstract Online Submission Closes - September 9, 2011
Hotel Reservations Close - September 15, 2011
Conference Registration Closes