A simple imputation method for longitudinal studies with non-ignorable non-responses.
Missing data are a common problem in longitudinal studies in the health sciences. Motivated by data from the Muscatine Coronary Risk Factor (MCRF) study, a longitudinal study of obesity, we propose a simple imputation method for handling non-ignorable non-responses (i.e., when non-response is related to the specific values that should have been obtained) in longitudinal studies with either discrete or continuous outcomes. In the proposed approach, two regression models are specified; one for the marginal mean of the response, the other for the conditional mean of the response given non-response patterns. Statistical inference for the model parameters is based on the generalized estimating equations (GEE) approach. An appealing feature of the proposed method is that it can be readily implemented using existing, widely-available statistical software. The method is illustrated using longitudinal data on obesity from the MCRF study.
Wang
Molin
M
Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. mwang@jimmy.harvard.edu
Fitzmaurice
Garrett M
GM
D000465:Algorithms; D015982:Bias; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D008137:Longitudinal Studies; D008954:Models, Biological; D015233:Models, Statistical