Bland-Altman Plot for Censored Variables.

Abstract

The comparison of two measurement methods turns out to be a statistical challenge if some of the observations are below the limit of quantification or detection. Here we show how the Bland-Altman plot can be modified for censored variables. The reference lines (bias and limits of agreement) in the Bland-Altman plot have to be estimated for censored variables. In a simulation study, we compared three different estimation methods: Restricting the data set to fully quantifiable pairs of observations (complete case analysis), naïvely substituting missing values with half of the limit of quantification, and a multiple imputation procedure based on a maximum likelihood approach for bivariate lognormally distributed variables with censoring. The results show that simple ad-hoc solutions may lead to bias in the results when comparing two measurement methods with censored observations, whereas the presented multiple imputation approach of the Bland-Altman method allows adequate consideration of censored variables. The method works similarly for other distribution assumptions.