ArticleComputational statistics & data analysis2014
Flexible Modeling of Survival Data with Covariates Subject to Detection Limits via Multiple Imputation.
Article in Computational statistics & data analysis, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Making Sense of Censored Covariates: Statistical Methods for Studies of Huntington's Disease.Annual review of statistics and its application · 2024Article
- Understanding the implications of a complete case analysis for regression models with a right-censored covariate.The American statistician · 2024Article
- Semiparametric analysis of a generalized linear model with multiple covariates subject to detection limits.Statistics in medicine · 2022Article
- The missing indicator approach for censored covariates subject to limit of detection in logistic regression models.Annals of epidemiology · 2019Article
- Cox regression model with randomly censored covariates.Biometrical journal. Biometrische Zeitschrift · 2019Article
- Estimation of indirect effect when the mediator is a censored variable.Statistical methods in medical research · 2018Article
- A joint model for mixed and truncated longitudinal data and survival data, with application to HIV vaccine studies.Biostatistics (Oxford, England) · 2018Article
- A Fast EM Algorithm for Fitting Joint Models of a Binary Response and Multiple Longitudinal Covariates Subject to Detection Limits.Computational statistics & data analysis · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Models for survival data generally assume that covariates are fully observed. However, in medical studies it is not uncommon for biomarkers to be censored at known detection limits. A computationally-efficient multiple imputation procedure for modeling survival data with covariates subject to detection limits is proposed. This procedure is developed in the context of an accelerated failure time model with a flexible seminonparametric error distribution. The consistency and asymptotic normality of the multiple imputation estimator are established and a consistent variance estimator is provided. An iterative version of the proposed multiple imputation algorithm that approximates the EM algorithm for maximum likelihood is also suggested. Simulation studies demonstrate that the proposed multiple imputation methods work well while alternative methods lead to estimates that are either biased or more variable. The proposed methods are applied to analyze the dataset from a recently-conducted GenIMS study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.