A1546
Title: SPARCC: Semi-parametric robust estimation in a right-censored covariate model
Authors: Seong-ho Lee - University of Seoul (Korea, South)
Tanya Garcia - UNC Chapel Hill (United States)
Karen Marder - Columbia University (United States)
Yanyuan Ma - Pennsylvania State University (United States)
Brian Richardson - University of North Carolina at Chapel Hill (United States) [presenting]
Abstract: In Huntington disease research, a current goal is to understand how symptoms change prior to a clinical diagnosis. Statistically, achieving this goal entails modeling symptom severity as a function of the Covariate time of diagnosis, which is often heavily right-censored in observational studies. Existing estimators that handle right-censored covariates, such as the complete case estimator and maximum likelihood estimator, vary in their statistical efficiency and robustness to misspecifications of nuisance parameters (i.e., densities for the censored Covariate and censoring variable). The SPARCC estimator (SemiPArametric Robust estimation in a right-Censored Covariate model) is proposed as a Robust and efficient alternative. When the nuisance parameters are modeled parametrically, the SPARCC estimator is doubly Robust, that is, consistent if at least one nuisance parameter is correctly specified, and SemiPArametric efficient if both are correctly specified. When the nuisance parameters are estimated via nonparametric or machine learning methods that converge sufficiently fast, the SPARCC estimator is consistent and SemiPArametric efficient. Empirical results demonstrate that the proposed estimator, implemented in the R package SPARCC, has its claimed properties. Application to Huntington disease symptom trajectories using data from the Enroll-HD study is presented.
