New Frontiers for Conditional Mean Imputation: Overcoming Censored Predictors to Model the Progression of Huntington's Disease

• Activity Number: 312 - What We Know About What We Don’t Know: Overcoming Incomplete Data in Practice
• Type: Invited
• Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
• Sponsor: ENAR
• Author(s): Sarah C. Lotspeich* and Kyle F. Grosser and Tanya P. Garcia
• Companies: University of North Carolina at Chapel Hill and University of North Carolina at Chapel Hill and University of North Carolina at Chapel Hill
• Keywords: Accelerated failure-time model; Nelson-Aalen estimator; Single imputation; Cox model; Neurodegenerative disease; Survival analysis

Abstract

Since discovering therapies that target the genetic root of Huntington's disease, researchers have investigated whether these therapies can slow or halt symptoms and, if so when is best to initiate treatment. Symptoms are most detectable before and after clinical diagnosis, but modeling their progression is problematic since the time to clinical diagnosis is often censored. This creates a pressing statistical challenge: modeling how symptoms (the outcome) change across time to clinical diagnosis (a censored predictor). Conditional mean imputation is an appealing strategy, replacing censored times of clinical diagnoses. However, despite efforts to make conditional mean imputation flexible, it still makes restrictive assumptions (like proportional hazards) that may be unrealistic. We develop a suite of extensions to conditional mean imputation by incorporating estimators for the conditional distribution of the censored predictor to offer more efficient and robust inference. We discuss in simulations when each version of conditional mean imputation is most appropriate and evaluate our methods in Huntington's disease data.