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 stories and the gathering of validity information from different assets. 3 chapters current equipment for corrections to handle choice bias, out of control confounding, and type error. 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 strategies for bias research were on hand 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 tools in a constant type and offers customizable spreadsheets to enforce the recommendations. via downloading the spreadsheets (available at hyperlinks supplied within the text), readers can persist with the examples within the textual content after which adjust the spreadsheet to accomplish their very own bias analyses. Readers with no event utilizing quantitative bias research may be in a position to layout, enforce, and comprehend bias analyses that deal with the key threats to the validity of epidemiologic learn. 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 healthiness and improvement, either on the Boston college university of Public well-being. Aliza okay. Fink is a venture supervisor at Macro foreign in Bethesda, Maryland. jointly they've got prepared and provided many day-long workshops at the tools of quantitative bias research. additionally, they've got collaborated on many papers that constructed equipment of quantitative bias research or used the equipment within the information research.
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Additional info for Applying Quantitative Bias Analysis to Epidemiologic Data
In assessing selection bias, the exposure and disease information are available for study participants, so the information required should be collected from nonparticipants. In contrast, information bias from classification error arises within the actual study population, so the data required for assessing information bias should be collected from a subset of participants (an internal validity study) or from a population similar to the participants (an external validity study). Careful consideration of the target population will lead to a more appropriate bias analysis.
Conducting a study that will yield a measure of association with as little bias as practical requires careful planning and choices in the design of data collection and analysis. Similarly, quantifying the amount of residual bias requires choices in the design of data collection and analysis. Since conducting a high-quality bias analysis follows the same steps as conducting a high-quality epidemiologic study, plans for both should be integrated at each phase of the study, as depicted in Fig. 1. When Is Quantitative Bias Analysis Valuable?
For example, the gold-standard 36 3 Data Sources for Bias Analysis method for measuring sobriety (dichotomized as above or below a threshold blood alcohol concentration) might be a laboratory assay of blood alcohol content. A less expensive, less time-consuming, or less invasive assay would be a breathalyzer test, which presumes a standard correlation between breath concentration of alcohol and blood concentration of alcohol. To the extent that the correlation varies across persons or within persons and across different breaths of air, the breathalyzer test would be a less perfect measure of blood alcohol concentration.