A new pre-print. Here, we review ways to address transportability problems when positivity is violated (i.e., there is a covariate that does not overlap between populations). We also propose a new way: a synthesis of statistical & simulation modeling
The more interesting contribution is the proposal of a way to combine statistical (e.g., g-methods) and simulation (e.g., mechanistic, math, microsim models)
Here, I will review the basic idea / motivation
In the paper, we have an illustrative example in STI testing. We want to generalize a trial to a clinic population. However, the trial was only conducted among men, but the clinic includes men and women
This violation of positivity prevents us from transporting
But let's consider the following structural model (where W=1 is women) provided in the image. Were this model known, then we could transport. However, we are only able to estimate the red part of the model using the data...
So we propose using a simulation model to fill-in the blue component. This simulation model is driven by external knowledge
In the paper, we show how the other two approaches to addressing positivity are special cases of the synthesis approach. G-computation and IPW estimators are proposed. Both are applied to an illustrative example and in simulations (code at link below)
https://github.com/pzivich/publications-code/tree/master/TransportNoPositivity
