ArticleThe American statistician2024
Understanding the implications of a complete case analysis for regression models with a right-censored covariate.
Article in The American statistician, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Epidemiology of children and adolescents undergoing surgery at Mexican public hospitals: a retrospective registry-based analysis from 2010 to 2022.BMC pediatrics · 2026Article
- SPARCC: Semi-Parametric Robust Estimation in a Right-Censored Covariate Model.Journal of the American Statistical Association · 2026Article
- Making Sense of Censored Covariates: Statistical Methods for Studies of Huntington's Disease.Annual review of statistics and its application · 2024Article
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Authors and funding
2 authors.
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Abstract
Despite its drawbacks, the complete case analysis is commonly used in regression models with incomplete covariates. Understanding when the complete case analysis will lead to consistent parameter estimation is vital before use. Our aim here is to demonstrate when a complete case analysis is consistent for randomly right-censored covariates and to discuss the implications of its use even when consistent. Across the censored covariate literature, different assumptions are made to ensure a complete case analysis produces a consistent estimator, which leads to confusion in practice. We make several contributions to dispel this confusion. First, we summarize the language surrounding the assumptions that lead to a consistent complete case estimator. Then, we show a unidirectional hierarchical relationship between these assumptions, which leads us to one sufficient assumption to consider before using a complete case analysis. Lastly, we conduct a simulation study to illustrate the performance of a complete case analysis with a right-censored covariate under different censoring mechanism assumptions, and we demonstrate its use with a Huntington disease data example.
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