T. Gorbach

A comparison of missing data approaches for linear regression with missing not at random outcome and predictors

This paper compares several approaches for handling MNAR data in linear regression when missingness depends on both partially observed outcomes and predictors and concludes that not-at-random fully conditional specification was straightforward to implement and yielded coverage close to the nominal level in most scenarios.