Optimal method for determining the intraclass correlation coefficients of urinary biomarkers such as dialkylphosphates from imputed data

Abstract

Urinary biomarkers are commonly used in epidemiological studies as surrogates or indicators of exposure to chemical substances. Evaluating the reliability of a biomarker is highly important because use of an unreliable marker may lead to misclassification and attenuation bias, resulting in flawed interpretations and conclusions. Although intraclass correlation coefficient (ICC) is regarded as a typical index of test reliability, methods for determining the ICCs of urinary biomarkers have not been standardised, and different methods have been used. This study evaluated different imputation methods for left-censored data, i.e., four imputation or one substitution methods, before calculating ICCs, and at the same time mathematically assessed the impact of the left-censoring proportion on the estimated ICCs. Biomarkers of exposure to organophosphate pesticides, i.e., dialkylphosphates, were used as an example. The Gibbs sampler-based left-censored missing value imputation approach had the best performance for imputation of values below reporting limits, with lower values on Kolmogorov–Smirnov test statistics than other imputation/substitution methods, i.e., a univariate distribution fitting approach, multiple imputation by chained equation, a bootstrap expectation–maximisation algorithm approach, and a single value substitution. In all imputation methods, however, ICCs decreased as censoring rates increased. We propose a method to estimate true ICCs based on mathematical estimation.