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