Estimating equations with nonignorably missing response data.
Troxel, Lipsitz, and Brennan (1997, Biometrics 53, 857-869) considered parameter estimation from survey data with nonignorable nonresponse and proposed weighted estimating equations to remove the biases in the complete-case analysis that ignores missing observations. This paper suggests two alternative modifications for unbiased estimation of regression parameters when a binary outcome is potentially observed at successive time points. The weighting approach of Robins, Rotnitzky, and Zhao (1995, Journal of the American Statistical Association 90, 106-121) is also modified to obtain unbiased estimating functions. The suggested estimating functions are unbiased only when the missingness probability is correctly specified, and misspecification of the missingness model will result in biases in the estimates. Simulation studies are carried out to assess the performance of different methods when the covariate is binary or normal. For the simulation models used, the relative efficiency of the two new methods to the weighting methods is about 3.0 for the slope parameter and about 2.0 for the intercept parameter when the covariate is continuous and the missingness probability is correctly specified. All methods produce substantial biases in the estimates when the missingness model is misspecified or underspecified. Analysis of data from a medical survey illustrates the use and possible differences of these estimating functions.
Wang
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YG
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. wgyang@hsph.harvard.edu
D001699:Biometry; D003625:Data Collection; D003627:Data Interpretation, Statistical; D005554:Forensic Medicine; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D017410:Practice Guidelines as Topic; D012044:Regression Analysis