Abstract
Various statistical tests on concentration data serve to support decision-making regarding characterization and monitoring of contaminated media, assessing exposure to a chemical, and quantifying the associated risks. However, the routine statistical protocols cannot be directly applied because of challenges arising from nondetects or left-censored observations that are concentration measurements below detection limit of measuring instruments. Despite the existence of techniques based on survival analysis that can adjust for nondetects, these are seldom taken into account properly. A comprehensive review of literature showed that managing policies regarding analysis of censored data do not always agree, and guidance from regulatory agencies may be outdated. Therefore, researchers and practitioners commonly resort to the most convenient way of tackling the censored data problem by substituting nondetects with arbitrary constants prior to data analysis, although this is generally regarded as a bias-prone approach. Hoping to improve the interpretation of concentration data, this paper aims to familiarize researchers in different disciplines with the significance of left-censored observations, and provides theoretical and computational recommendations (under both frequentist and Bayesian framework) for adequate analysis of censored data. In particular, this paper synthesizes key findings from previous research with respect to three noteworthy aspects of inferential statistics, including estimation of descriptive statistics, hypothesis testing, and regression analysis. This article is protected by copyright. All rights reserved
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