Using auxiliary data for parameter estimation with non-ignorably missing outcomes

We propose a method for estimating parameters in generalized linear models when the outcome variable is missing for some subjects and the missing data mechanism is non-ignorable. We assume throughout that the covariates are f ully observed. One possible method for estimating the parameters is maximum likelihood with a non-ignorable missing data model. However, caution must be used when fitting non-ignorable missing data models because certain para meters may be inestimable for some models. Instead of fitting a non-ignorab le model, we propose the use of auxiliary information in a likelihood appro ach to reduce the bias, without having to specify a non-ignorable model. Th e method is applied to a mental health study.