Logistic Regression Models for Binary Panel Data with Attrition

Garrett M. Fitzmaurice, Anthony F. Heath and Peter Clifford

Journal of the Royal Statistical Society Series A, 1996, vol. 159, issue 2, 249-263

Abstract: 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.

Date: 1996
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