Semiparametric Prediction under Right-Censored Covariates

Thomas M. Menino Convention & Exhibition Center 

We develop a semiparametric prediction method for outcomes with right-censored covariates. Fixing a nominal coverage level and an interval center, we estimate the interval length via a semiparametric approach using an efficient influence function. The estimator is semiparametrically efficient, yielding highly accurate prediction intervals, and is doubly robust to misspecification of nuisance models, allowing flexible choices for the nuisance models. Moreover, we establish asymptotic validity for the resulting prediction coverage. Simulations and a Huntington disease application show that our method produces prediction intervals with more stable interval lengths and prediction coverage closer to the nominal level, compared to distribution-free baseline methods such as conformal prediction.

semiparametric modeling

double robustness

semiparametric efficiency

censored covariates

conformal prediction

Huntington disease 

Biometrics Section