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Measuring association among censored antibody titer data

Censoring due to a limit of detection or limit of quantification happens quite often in many medical studies. Conventional approaches to deal with censoring when analyzing these data include, for example, the substitution method and the complete case (CC) analysis. More recently, maximum likelihood...

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Autores principales: Tran, Thao M. P., Abrams, Steven, Aerts, Marc, Maertens, Kirsten, Hens, Niel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8251995/
https://www.ncbi.nlm.nih.gov/pubmed/33942345
http://dx.doi.org/10.1002/sim.8995
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author Tran, Thao M. P.
Abrams, Steven
Aerts, Marc
Maertens, Kirsten
Hens, Niel
author_facet Tran, Thao M. P.
Abrams, Steven
Aerts, Marc
Maertens, Kirsten
Hens, Niel
author_sort Tran, Thao M. P.
collection PubMed
description Censoring due to a limit of detection or limit of quantification happens quite often in many medical studies. Conventional approaches to deal with censoring when analyzing these data include, for example, the substitution method and the complete case (CC) analysis. More recently, maximum likelihood estimation (MLE) has been increasingly used. While the CC analysis and the substitution method usually lead to biased estimates, the MLE approach appears to perform well in many situations. This article proposes an MLE approach to estimate the association between two measurements in the presence of censoring in one or both quantities. The central idea is to use a copula function to join the marginal distributions of the two measurements. In various simulation studies, we show that our approach outperforms existing conventional methods (CC and substitution analyses). In addition, rank‐based measures of global association such as Kendall's tau or Spearman's rho can be studied, hence, attention is not only confined to Pearson's product‐moment correlation coefficient capturing solely linear association. We have shown in our simulations that our approach is robust to misspecification of the copula function or marginal distributions given a small association. Furthermore, we propose a straightforward MLE method to fit a (multiple) linear regression model in the presence of censoring in a covariate or both the covariate and the response. Given the marginal distribution of the censored covariate, our method outperforms conventional approaches. We also compare and discuss the performance of our method with multiple imputation and missing indicator model approaches.
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spelling pubmed-82519952021-07-07 Measuring association among censored antibody titer data Tran, Thao M. P. Abrams, Steven Aerts, Marc Maertens, Kirsten Hens, Niel Stat Med Research Articles Censoring due to a limit of detection or limit of quantification happens quite often in many medical studies. Conventional approaches to deal with censoring when analyzing these data include, for example, the substitution method and the complete case (CC) analysis. More recently, maximum likelihood estimation (MLE) has been increasingly used. While the CC analysis and the substitution method usually lead to biased estimates, the MLE approach appears to perform well in many situations. This article proposes an MLE approach to estimate the association between two measurements in the presence of censoring in one or both quantities. The central idea is to use a copula function to join the marginal distributions of the two measurements. In various simulation studies, we show that our approach outperforms existing conventional methods (CC and substitution analyses). In addition, rank‐based measures of global association such as Kendall's tau or Spearman's rho can be studied, hence, attention is not only confined to Pearson's product‐moment correlation coefficient capturing solely linear association. We have shown in our simulations that our approach is robust to misspecification of the copula function or marginal distributions given a small association. Furthermore, we propose a straightforward MLE method to fit a (multiple) linear regression model in the presence of censoring in a covariate or both the covariate and the response. Given the marginal distribution of the censored covariate, our method outperforms conventional approaches. We also compare and discuss the performance of our method with multiple imputation and missing indicator model approaches. John Wiley and Sons Inc. 2021-05-03 2021-07-20 /pmc/articles/PMC8251995/ /pubmed/33942345 http://dx.doi.org/10.1002/sim.8995 Text en © 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Tran, Thao M. P.
Abrams, Steven
Aerts, Marc
Maertens, Kirsten
Hens, Niel
Measuring association among censored antibody titer data
title Measuring association among censored antibody titer data
title_full Measuring association among censored antibody titer data
title_fullStr Measuring association among censored antibody titer data
title_full_unstemmed Measuring association among censored antibody titer data
title_short Measuring association among censored antibody titer data
title_sort measuring association among censored antibody titer data
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8251995/
https://www.ncbi.nlm.nih.gov/pubmed/33942345
http://dx.doi.org/10.1002/sim.8995
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