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Full-Text Articles in Statistics and Probability
New Estimating Methods For Surrogate Outcome Data, Bin Nan
New Estimating Methods For Surrogate Outcome Data, Bin Nan
The University of Michigan Department of Biostatistics Working Paper Series
Surrogate outcome data arise frequently in medical research. The true outcomes of interest are expensive or hard to ascertain, but measurements of surrogate outcomes (or more generally speaking, the correlates of the true outcomes) are usually available. In this paper we assume that the conditional expectation of the true outcome given covariates is known up to a finite dimensional parameter. When the true outcome is missing at random, the e±cient score function for the parameter in the conditional mean model has a simple form, which is similar to the generalized estimating functions. There is no integral equation involved as in …
Semiparametric Regression Models With Missing Data: The Mathematics In The Work Of Robins Et Al., Menggang Yu, Bin Nan
Semiparametric Regression Models With Missing Data: The Mathematics In The Work Of Robins Et Al., Menggang Yu, Bin Nan
The University of Michigan Department of Biostatistics Working Paper Series
This review is an attempt to understand the landmark papers of Robins, Rotnitzky, and Zhao (1994) and Robins and Rotnitzky (1992). We revisit their main results and corresponding proofs using the theory outlined in the monograph by Bickel, Klaassen, Ritov, and Wellner (1993). We also discuss an illustrative example to show the details of applying these theoretical results.