Logistic Regression Models for Binary Panel Data with Attrition

We discuss ways of analysing panel data when the response is binary and there is attrition or drop-out. In general, informative or non-ignorable drop-out models are non-identifiable and arbitrary constraints on the drop-out model must be imposed before carrying out a statistical analysis. The problem is particularly acute when predictors as well as response variables are lost by attrition. We describe a likelihood-based method for dealing with the drop-out process in this difficult case and show how the effect of non-identifiability can be reduced by importing additional data from a cross-sectional survey of the same population. The methods are primarily motivated by data from the 1987-92 British Election Panel Study and the 1992 British Election Study.