Citation:

George, BJ, L. Gains-Germain, K. Broms, K. Black, M. Furman, M. Hays, K. Thomas, AND Jane Ellen Simmons. Censoring Trace-Level Environmental Data: Statistical Analysis Considerations to Limit Bias. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 55(6):3786-3795, (2021). https://doi.org/10.1021/acs.est.0c02256

Impact/Purpose:

This manuscript addresses the important problem that health researchers across all disciplines are often limited in understanding the impact(s) of trace-level environmental chemicals on either exposure or toxicity because of conventional censoring and statistical analysis practices. Long-standing practices often result in low concentration environmental data being overlooked or analyzed inadequately despite being fundamentally valuable for risk assessment and health research. Investigators and data users across all research disciplines should remain on guard against censoring-related bias and consider statistical analysis approaches readily available in modern software.

Description:

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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