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Annual Review of Statistics and Its Application

Volume 11, 2024

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

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.

Keyword(s): censored covariate, Huntington's disease, imputation, inverse probability weighting, likelihood, survival analysis, thresholding
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/content/journals/10.1146/annurev-statistics-040522-095944
2024-04-22
2024-05-11
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Making Sense of Censored Covariates: Statistical Methods for Studies of Huntington's Disease
Annual Review of Statistics and Its Application 11, 255 (2024); https://doi.org/10.1146/annurev-statistics-040522-095944
/content/journals/10.1146/annurev-statistics-040522-095944
/content/journals/10.1146/annurev-statistics-040522-095944
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