Handling of dioxin measurement data in the presence of non-detectable values: overview of available methods and their application in the Seveso chloracne study.
Exposure measurements of concentrations that are non-detectable or near the detection limit (DL) are common in environmental research. Proper statistical treatment of non-detects is critical to avoid bias and unnecessary loss of information. In the present work, we present an overview of possible statistical strategies for handling non-detectable values, including deletion, simple substitution, distributional methods, and distribution-based imputation. Simple substitution methods (e.g., substituting 0, DL/2, DL/ radical2, or DL for the non-detects) are the most commonly applied, even though the EPA Guidance for Data Quality Assessment discouraged their use when the percentage of non-detects is >15%. Distribution-based multiple imputation methods, also known as robust or "fill-in" procedures, may produce dependable results even when 50-70% of the observations are non-detects and can be performed using commonly available statistical software. Any statistical analysis can be conducted on the imputed datasets. Results properly reflect the presence of non-detectable values and produce valid statistical inference. We describe the use of distribution-based multiple imputation in a recent investigation conducted on subjects from the Seveso population exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), in which 55.6% of plasma TCDD measurements were non-detects. We suggest that distribution-based multiple imputation be the preferred method to analyze environmental data when substantial proportions of observations are non-detects.
Baccarelli
Andrea
A
Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, 6120 Executive Boulevard, Bethesda, MD 20852, USA. andrea.baccarelli@unimi.it
Pfeiffer
Ruth
R
Consonni
Dario
D
Pesatori
Angela C
AC
Bonzini
Matteo
M
Patterson
Donald G
DG
Bertazzi
Pier Alberto
PA
Landi
Maria Teresa
MT
D000152:Acne Vulgaris / Q000139:chemically induced; D002623:Chemistry Techniques, Analytical / Q000379:methods; D003627:Data Interpretation, Statistical; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D006801:Humans; D007558:Italy; D016014:Linear Models; D000072317:Polychlorinated Dibenzodioxins / Q000097:blood*
D000072317:Polychlorinated Dibenzodioxins