INFERENCE USING SURROGATE OUTCOME DATA AND A VALIDATION SAMPLE

In the context of estimating beta from the regression model P(beta) (Y\X), relating response Y to covariates X, suppose that only a surrogate response S is available for most study subjects. Suppose that for a random subsample of the study cohort, termed the validation sample, the true outcome Y is available in addition to S. We consider maximum likelihood estimation of beta from such data and show that it is nonrobust to misspecification of the distribution relating the surrogate to the true outcome, P(S\Y, X). An alternative semiparametric method is also considered, which is nonparametric with respect to P(S\Y, X). Large-sample distribution theory for maximum estimated likelihood estimates is developed. An illustrative example is presented.