Applying Quantitative Bias Analysis to Epidemiologic Data by Timothy L. Lash

By Timothy L. Lash

This textual content offers the first-ever compilation of bias research equipment to be used with epidemiologic information. It courses the reader during the making plans phases of bias research, together with the layout of validation reports and the gathering of validity info from different resources. 3 chapters current equipment for corrections to deal with choice bias, out of control confounding, and category error. next chapters expand those easy methods 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 concepts for bias research were to be had for many years, those equipment 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 model and offers customizable spreadsheets to enforce the options. through downloading the spreadsheets (available at hyperlinks supplied within the text), readers can keep on with the examples within the textual content after which regulate the spreadsheet to accomplish their very own bias analyses. Readers with out adventure utilizing quantitative bias research could 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 tools 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 overall healthiness and improvement, either on the Boston college institution of Public overall healthiness. Aliza okay. Fink is a undertaking supervisor at Macro foreign in Bethesda, Maryland. jointly they've got geared up and awarded many day-long workshops at the tools of quantitative bias research. additionally, they've got collaborated on many papers that built equipment of quantitative bias research or used the equipment within the facts research.

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Sample text

When estimates of association are calculated as a part of the bivariate comparison, the analysis is also called a stratified analysis. Often one comparison is a focus of the stratified analysis, which is the comparison of the disease proportions in those exposed to the agent of interest with those unexposed to the agent of interest. This comparison relates directly to the original objective: a valid and precise estimate of the effect of an exposure on the occurrence of a disease. To continue the stratified analysis, the comparisons of disease proportions in exposed versus unexposed are expanded to comparisons within levels of other analytic variables.

2003) investigated the association between little league injury claims and type of baseball used (safety baseball vs traditional baseball). 93). They were concerned that injuries might be less likely to be reported when safety baseballs were used than when traditional baseballs were used, which would create a biased estimate of a protective effect. To conduct a simple bias analysis, they estimated that no more than 30% of injuries were unreported and that the difference in reporting rates was no more than 10% (the bias parameters).

Quantifying Error 25 Type of Data: Record-Level Versus Summary Many of the techniques for quantitative bias analysis described herein assume that the investigator has access to record-level data. That is, they assume that the original data set with information on each subject in the study is available for analysis. Record-level data, or original data, allow for a wider range of methods for quantitative bias analysis. With record-level data, corrections for classification errors can be made at the level of the individual subjects, which preserves correlations between the study variables and allows the analyst to adjust the corrected estimates of association for other confounders.

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