Making Sense of Censored Covariates: Statistical Methods for Studies of Huntington’s Disease

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Citation

Lotspeich, Sarah C.; Ashner, Marissa C.; Vazquez, Jesus E.; Richardson, Brian D.; Grosser, Kyle F.; Bodek, Benjamin E.; & Garcia, Tanya P. (2023). Making Sense of Censored Covariates: Statistical Methods for Studies of Huntington's Disease. Annual Review of Statistics and Its Application.

Abstract

The landscape of survival analysis is constantly being revolutionized to answer biomedical challenges, most recently the statistical challenge of censored covariates rather than outcomes. There are many promising strategies to tackle censored covariates, including weighting, imputation, maximum likelihood, and Bayesian methods. Still, this is a relatively fresh area of research, different from the areas of censored outcomes (i.e., survival analysis) or missing covariates. In this review, we discuss the unique statistical challenges encountered when handling censored covariates and provide an in-depth review of existing methods designed to address those challenges. We emphasize each method's relative strengths and weaknesses, providing recommendations to help investigators pinpoint the best approach to handling censored covariates in their data.

URL

https://doi.org/10.1146/annurev-statistics-040522-095944

Reference Type

Journal Article

Year Published

2023

Journal Title

Annual Review of Statistics and Its Application

Author(s)

Lotspeich, Sarah C.
Ashner, Marissa C.
Vazquez, Jesus E.
Richardson, Brian D.
Grosser, Kyle F.
Bodek, Benjamin E.
Garcia, Tanya P.