Measuring association among censored antibody titer data

• Published in: Statistics in medicine Vol. 40; no. 16; pp. 3740 - 3761
• Main Authors: Tran, Thao M. P., Abrams, Steven, Aerts, Marc, Maertens, Kirsten, Hens, Niel
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 20.07.2021; John Wiley and Sons Inc
• Subjects: antibody titers; association; geometric mean concentration; left‐censored data; maximum likelihood inference; association; antibody titers; geometric mean concentration; left-censored data; maximum likelihood inference
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.8995

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.