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Separating Measurement Error and Signal in Environmental Data: Use of Replicates to Address Uncertainty
Measurement uncertainty has long been a concern in the characterizing and interpreting environmental and toxicological measurements. We compared statistical analysis approaches when there are replicates: a Naı̈ve approach that omits replicates, a Hybrid approach that inappropriately treats replicates as independent samples, and a Measurement Error Model (MEM) approach in a random effects analysis of variance (ANOVA) model that appropriately incorporates replicates.
Response to Comment on “Censoring Trace-Level Environmental Data: Statistical Analysis Considerations to Limit Bias”
We appreciate the thoughtful commentary and opportunity to continue the discussion on this important topic of dealing with left-censored data. We thank Prof. Hites for reflecting on our introduction and want to reinforce that the challenges for measurement of trace-level environmental data and their statistical analysis are long-standing, complex, and continually evolving.
Censoring Trace-Level Environmental Data: Statistical Analysis Considerations to Limit Bias
Trace-level environmental data typically include values near or below detection and quantitation thresholds where health effects may result from low-concentration exposures to one chemical over time or to multiple chemicals.
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