Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Bias analysis applied to Agricultural Health Study publications to estimate non-random sources of uncertainty | Journal of Occupational Medicine and Toxicology BioMed Central Open Access Bias analysis applied to Agricultural Health Study publications to estimate non-random sources of uncertainty Timothy L Lash Address Department of Epidemiology Boston University School of Public Health 715 Albany St. TE3 Boston MA USA Email Timothy L Lash - tlash@ Published 26 November 2007 Received I I June 2007 . . . _ . _ . .- . - .r _ Accepted 26 November 2007 Journal of Occupational Medicine and Toxicology 2007 2 15 doi 1745-6673-2-15 This article is available from http content 2 1 15 2007 Lash licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background The associations of pesticide exposure with disease outcomes are estimated without the benefit of a randomized design. For this reason and others these studies are susceptible to systematic errors. I analyzed studies of the associations between alachlor and glyphosate exposure and cancer incidence both derived from the Agricultural Health Study cohort to quantify the bias and uncertainty potentially attributable to systematic error. Methods For each study I identified the prominent result and important sources of systematic error that might affect it. I assigned probability distributions to the bias parameters that allow quantification of the bias drew a value at random from each assigned distribution and calculated the estimate of effect adjusted for the biases. By repeating the draw and adjustment process over multiple iterations I generated a frequency distribution of adjusted results from which I obtained a point estimate and simulation interval. These methods were applied without access to the primary record-level dataset. Results The conventional .
