Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series

We consider assessment of nonresponse bias for the mean of a survey variable Y subject to nonresponse. We assume that there are a set of covariates observed for nonrespondents and respondents. To reduce dimensionality and for simplicity we reduce the covariates to a proxy variable X that has the highest correlation with Y, estimated from a regression analysis of respondent data. We consider adjusted estimators of the mean of Y that are maximum likelihood for a pattern-mixture model with different mean and covariance matrix of Y and X for respondents and nonrespondents, assuming missingness is an arbitrary function of a known linear combination of X and Y. We propose a taxonomy for the evidence concerning bias based on the strength of the proxy and the deviation of the mean of X for respondents from its overall mean, propose a sensitivity analysis, and describe Bayesian versions of this approach. We propose using the fraction of missing information from multiple imputation under the pattern-mixture model as a measure of nonresponse bias. Methods are demonstrated through simulation and data from the third National Health and Nutrition Examination Survey (NHANES III).

Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.