Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/34055
Title: | Measuring association among censored antibody titer data | Authors: | TRAN, Mai Phuong Thao ABRAMS, Steven AERTS, Marc Maertens, Kirsten HENS, Niel |
Issue Date: | 2021 | Publisher: | Source: | Statistics in Medicine, 40 (16), p. 3740-3761 | Abstract: | 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. | Keywords: | Association;Left-censored data;Antibody titres;Geometric Mean Concentration;Maximum Likelihood Inference | Document URI: | http://hdl.handle.net/1942/34055 | ISSN: | 0277-6715 | e-ISSN: | 1097-0258 | DOI: | 10.1002/sim.8995 | ISI #: | 000646403100001 | Rights: | This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs 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. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Statistics in Medicine - 2021 - Tran - Measuring association among censored antibody titer data.pdf | Published version | 1.07 MB | Adobe PDF | View/Open |
WEB OF SCIENCETM
Citations
6
checked on Oct 17, 2024
Page view(s)
42
checked on Jun 1, 2022
Download(s)
12
checked on Jun 1, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.