SPYCE: Semi-Parametric Y-dependent right-Censored Covariate Estimator

Music City Center 

Slow progression of Huntington's disease often hinders estimating how symptoms change over time due to right-censoring, where patients may not visit after a certain period. The censoring mechanism may depend on disease severity, with severe patients more likely to exit studies early. In this outcome-dependent censoring scenario, existing estimators fail to achieve consistency or require correct estimation of the models. We propose SPYCE, a doubly robust semiparametric estimator, which is consistent even when one of the models is misspecified. Utilizing semiparametric theory, we show that SPYCE is consistent and asymptotically normal for parametric nuisance models, having the smallest variance when both nuisance parameters are consistently estimated. Through kernel estimation and inverse probability weighting, we introduce flexibility in the nuisance models while retaining the same results. Simulation studies confirm the efficiency and double robustness of SPYCE compared to existing methods. Finally, analyzing the PREDICT-HD dataset, we discover that SPYCE gives different results about how symptoms change over time compared to conventional methods that are prone to error.

semiparametric modeling

right-censoring

double robustness

inverse probability weighting

kernel estimation

Huntington's disease 

Biometrics Section