A comparison of techniques for assessing central tendency in left-censored data using PCB and p,p′DDE contaminant concentrations from Michigan’s Bald Eagle Biosentinel Program

Abstract

Monitoring of contaminants in the environment is an important part of understanding the fate of ecosystems after a chemical insult. Frequently, such monitoring efforts result in datasets with observations below the detection limit (DL) that are reported as ‘non-detect’ or ‘<DL’ and no value is provided. This study explored the effects of non-detect data and their treatment on summary statistics. The data analyzed in this paper are real-world data. They consist of both large ( N = 234) and moderate ( n = 12–64) sample sizes with both good and marginal fit to an assumed distribution with a log-transformation. Summary statistics were calculated using (1) the ‘0.0001’ near-zero method of substitution, (2) substitution with ‘1/2 * DL’, (3) multiple imputation, and (4) Kaplan–Meier estimation. Median was used for comparison. Several analytical options for datasets with non-detect observations are available. The general consensus is that substitution methods ((1) and (2)) can produce biased summary statistics, especially as levels of substitution increase. Substitution methods continue to be used in research, likely because they are easy to implement. The objectives were to (1) assess the fit of lognormal distribution to the data, (2) compare and contrast the performance of four analytical treatments of left-censored data in terms of estimated geometric mean and standard error, and (3) make recommendations based on those results.