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A SURVEY OF LABORATORY AND STATISTICAL ISSUES RELATED TO FARMWORKER EXPOSURE STUDIES
Citation:
BARR, D. B., D. LANDSITTEL, M. NISHIOKA, K. W. THOMAS, B. CURWIN, J. H. RAYMER, K. C. DONNELLY, L. MCCAULEY, AND B. RYAN. A SURVEY OF LABORATORY AND STATISTICAL ISSUES RELATED TO FARMWORKER EXPOSURE STUDIES. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, 114(6):961-968, (2006).
Impact/Purpose:
Objectives for this task include the following:
o Investigate the current research on biomarkers of exposure for humans (ILSI documents).
o Develop better measurement methods and procedures for collecting biomarkers of exposure in human research studies.
o Conduct advanced statistical analyses on existing urinary biomarker data generated from EPA funded studies.
o Identify and provide valuable data inputs for modelers in their toxicokinetic and exposure models.
o Use modeling tools to understand the exposures of humans to chemicals related to biomarkers of exposure.
o Form a collaborative partnership with CDC to develop analytical methods for chemicals used as biomarkers of exposure in humans.
o Develop statistical methods and models to assess human exposures to chemicals of concern.
Description:
Developing internally valid, and perhaps generalizable, farmworker exposure studies is a complex process that involves many statistical and laboratory considerations. Statistics are an integral component of each study beginning with the design stage and continuing to the final data analysis and interpretation. Similarly, data quality plays a significant role in the overall value of the study. Data quality can be derived from several experimental parameters including statistical design of the study and quality of environmental and biological analytical measurements. We discuss statistical and analytic issues that should be addressed in every farmworker study. These issues include study design and sample size determination, analytical methods and quality control and assurance, treatment of missing data or data below the method's limits of detection, and post-hoc analyses of data from multiple studies.