Journal
ENVIRONMENTAL SCIENCE & TECHNOLOGY
Volume 55, Issue 6, Pages 3786-3795Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.est.0c02256
Keywords
nondetect; detection limit; reporting level; PAH; maximum likelihood estimation; regression on order statistics; simulation study; estimating the mean
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Funding
- U.S. EPA, through its Office of Research and Development [EP-C-13-022, EP-C-18-007]
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The study found that after-censoring statistical approaches were superior to threshold/2 substitution in analyzing environmental data. All after-censoring approaches were found to be inferior to including all measurement data in analysis. Investigators should be cautious of censoring-related bias stemming from distributional and analysis approach decisions.
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. In a cook stove case study, bias in dibenzo[a,h]anthracene concentration means and standard deviations (SDs) was assessed following censoring at thresholds for selected analysis approaches: substituting threshold/2, maximum likelihood estimation, robust regression on order statistics, Kaplan-Meier, and omitting censored observations. Means and SDs for gas chromatography-mass spectrometry-determined concentrations were calculated after censoring at detection and calibration thresholds, 17% and 55% of the data, respectively. Threshold/2 substitution was the least biased. Measurement values were subsequently simulated from two log-normal distributions at two sample sizes. Means and SDs were calculated for 30%, 50%, and 80% censoring levels and compared to known distribution counterparts. Simulation results illustrated (1) threshold/2 substitution to be inferior to modern after-censoring statistical approaches and (2) all after-censoring approaches to be inferior to including all measurement data in analysis. Additionally, differences in stove-specific group means were tested for uncensored samples and after censoring. Group differences of means tests varied depending on censoring and distributional decisions. Investigators should guard against censoring-related bias from (explicit or implicit) distributional and analysis approach decisions.
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