When randomized experiments are infeasible, analysts must rely on observational data in which treatment (or exposure) is not randomly assigned. Although randomized trials are the gold standard, there are many important epidemiological questions that can be addressed using observational data. Drawing unbiased inferences from such data relies on the use of appropriate statistical methods, such as causal inference methods, to account for the non-randomized design. This workshop will introduce the potential outcomes framework and the use of inverse probability (or propensity) of treatment weights (IPTW) to estimate causal effects. We will present step-by-step guidelines on how to estimate and perform diagnostic checks of the weights for settings with two or more treatment groups and for continuous exposures. We will provide an overview on how to implement omitted variable analyses, which are critical to any IPTW analysis as the robustness of causal effects depends on no unobserved confounders. Attendees will gain hands-on experience estimating each type of weight using gradient (or generalized) boosting models (GBM), as well as in how to estimate the causal effects of interest using the IPTW. Running these analyses can be done via the TWANG package/suite of commands in Stata, SAS, or R; code will be shared. We will showcase a new menu-driven free Shiny app. Attendees should be familiar with linear and logistic regression, but prior knowledge of IPTW and GBM is not necessary.
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