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Response to comment on Censoring Trace-Level Environmental Data: Statistical Analysis Considerations to Limit Bias
Citation:
George, Barbara Jane, Kent W. Thomas, AND Jane Ellen Simmons. Response to comment on Censoring Trace-Level Environmental Data: Statistical Analysis Considerations to Limit Bias. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 55(22):15556-15557, (2021). https://doi.org/10.1021/acs.est.1c06431
Impact/Purpose:
We agree with Prof. Hites’ guidance on the importance of designing studies to use analytical methods with sufficient sensitivity to prevent excess nondetects when such methods are available or can be readily developed. We view the greatest benefit will derive from coupling his advice with this advice: collaborate with well-qualified statisticians beginning at experimental design and report, along with the analytical methods used in calculations, the limits of detection, any censoring thresholds, all data quality indicators, and all individual measurement data values, appropriately flagged, so that modern statistical methods (e.g., maximum likelihood estimation) in readily available software can be used, limiting bias introduced by censored nondetects.
Description:
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. While there is extensive literature that addresses estimation of the mean in the presence of nondetects, there are relatively few recent papers describing advances in statistical approaches and software capability. We chose to build on the literature by assessing bias in means for Type I left-censored data, using freely available modern statistical software.
URLs/Downloads:
DOI: Response to comment on Censoring Trace-Level Environmental Data: Statistical Analysis Considerations to Limit Bias