|Title:||Regression Models For Selection Bias, Nonrandom Exposure, and Unobserved Confounding Using Stata|
|Presenter:||Chuck Huber, PhD, Senior Statistician, StataCorp|
|Where:||SER 2018 Annual Meeting|
|Date:||Tuesday, June 19, 2017|
|Time:||5:30 –7:30 p.m.|
Observational data often have issues that present challenges for the data
analyst. Data are sometimes missing not at random (MNAR), which can lead to
sample selection bias. The exposure of interest or treatment status is often
not assigned randomly. And many statistical models for these data must account
for unobserved confounding.
This talk will demonstrate how to use standard maximum-likelihood estimation to fit extended regression models (ERMs) that deal with all these common issues alone or simultaneously.