Analysis of semiparametric regression models for repeated outcomes in the presence of missing data

@article{Robins1995AnalysisOS,
  title={Analysis of semiparametric regression models for repeated outcomes in the presence of missing data},
  author={James M. Robins and Andrea Rotnitzky and Lue Ping Zhao},
  journal={Journal of the American Statistical Association},
  year={1995},
  volume={90},
  pages={106-121}
}
Abstract We propose a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on a vector of explanatory variables in the presence of missing response data. The proposed estimators do not require full specification of the likelihood. They can be viewed as an extension of generalized estimating equations estimators that allow for the data to be missing at random but not missing… Expand
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