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Proxy Pattern-Mixture Analysis For A Binary Variable Subject To Nonresponse., Rebecca H. Andridge, Roderick J. Little

The University of Michigan Department of Biostatistics Working Paper Series

We consider assessment of the impact of nonresponse for a binary survey

variable Y subject to nonresponse, when there is a set of covariates

observed for nonrespondents and respondents. To reduce dimensionality and

for simplicity we reduce the covariates to a continuous proxy variable X

that has the highest correlation with Y, estimated from a probit

regression analysis of respondent data. We extend our previously proposed

proxy-pattern mixture analysis (PPMA) for continuous outcomes to the binary

outcome using a latent variable approach. The method does not assume data

are missing at random, and creates a framework for sensitivity analyses.

Maximum …