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 likelihood, Bayesian, and multiple imputation versions of PPMA are
described, and robustness of these methods to model assumptions are
discussed. Properties are demonstrated through simulation and with data from
the Ohio Family Health Survey (OFHS).
Biostatistics | Social and Behavioral Sciences
Andridge, Rebecca H. and Little, Roderick J., "Proxy Pattern-Mixture Analysis for a Binary Variable Subject to Nonresponse." (November 2011). The University of Michigan Department of Biostatistics Working Paper Series. Working Paper 94.