By Timothy L. Lash
This textual content offers the first-ever compilation of bias research tools to be used with epidemiologic info. It courses the reader in the course of the making plans phases of bias research, together with the layout of validation experiences and the gathering of validity facts from different resources. 3 chapters current equipment for corrections to handle choice bias, out of control confounding, and type mistakes. next chapters expand those how to multidimensional bias research, probabilistic bias research, and a number of bias research. The textual content concludes with a bankruptcy on presentation and interpretation of bias research results.
Although ideas for bias research were to be had for many years, those tools are thought of tricky to enforce. this article not just gathers the equipment into one cohesive and arranged presentation, it additionally explains the equipment in a constant style and offers customizable spreadsheets to enforce the strategies. through downloading the spreadsheets (available at hyperlinks supplied within the text), readers can stick to the examples within the textual content after which alter the spreadsheet to accomplish their very own bias analyses. Readers with no event utilizing quantitative bias research could be in a position to layout, enforce, and comprehend bias analyses that tackle the foremost threats to the validity of epidemiologic examine. more matured analysts will worth the compilation of bias research equipment and hyperlinks to software program instruments that facilitate their projects.
Timothy L. Lash is an affiliate Professor of Epidemiology and Matthew P. Fox is an Assistant Professor within the heart for overseas well-being and improvement, either on the Boston collage tuition of Public wellbeing and fitness. Aliza ok. Fink is a venture supervisor at Macro foreign in Bethesda, Maryland. jointly they've got prepared and offered many day-long workshops at the equipment of quantitative bias research. additionally, they've got collaborated on many papers that built equipment of quantitative bias research or used the tools within the facts research.
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Additional resources for Applying Quantitative Bias Analysis to Epidemiologic Data
2002) investigated the effectiveness of 5-fluorouracil adjuvant chemotherapy in treating elderly colorectal cancer patients. 73). The investigators were concerned about bias from confounding by indication because the therapy assignment was not randomized. To assess the potential impact of this unmeasured confounder, they made assumptions about the range of (1) the prevalence of an unknown binary confounder, (2) the association between the confounder and colorectal mortality, and (3) the association between the confounder and receipt of 5-flourouracil therapy (these are the bias parameters).
In particular, melanoma cases might recall or report their history of sunlight exposure differently than controls sampled from the source population. This threat to validity would be an example of measurement error (Chap. 6), which can also be addressed by quantitative bias analysis. To implement a bias analysis, the investigators would require estimates of the sensitivity and specificity of sunlight exposure classification among melanoma cases and among members of the source population. , comparing self-report of sunlight exposure history with a diary of sunlight exposure kept by melanoma cases and noncases in a similar second population).
Quantitative bias analysis is therefore most valuable when studies yield narrow conventional confidence intervals – so have little residual random error – and when these studies are susceptible to a limited number of systematic errors. Such studies often appear to be an adequate basis for inference or for policy action, even though only random error has been quantified by the conventional confidence interval. Quantification of the error due to the limited number of biases will safeguard against inference or policy action that takes account of only random error.