Estimating Treatment Effects Using Longitudinal Data
*Miguel Hernan, Harvard School of Public Health 

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.