Applying Quantitative Bias Analysis to Epidemiologic Data

Use this page to download the accompanying spreadsheets and SAS code  (see bottom of page) for the book:

           Fox MP, MacLehose R, Lash TL. Applying Quantitative Bias Analysis to Epidemiologic Data.  Springer. 2021. Edition 2.

  Lash TL, Fox MP, Fink AK. Applying Quantitative Bias Analysis to Epidemiologic Data. 

           Springer. 2009. Edition 1.

You can find reviews of the first edition of the textbook in the American Journal of Epidemiology, JASA, JRSS and Biometrics.

This book collects and synthesizes methods for quantifying systematic errors that affect observational epidemiologic research.

This text provides the first-ever compilation of bias analysis methods for use with epidemiologic data. It guides the reader through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and classification errors. Subsequent chapters extend these methods to multidimensional bias analysis, probabilistic bias analysis, and multiple bias analysis. The text concludes with a chapter on presentation and interpretation of bias analysis results.

Although techniques for bias analysis have been available for decades, these methods are considered difficult to implement. This text not only gathers the methods into one cohesive and organized presentation, it also explains the methods in a consistent fashion and provides customizable spreadsheets, SAS and R code to implement the solutions. By downloading the code (available below), readers can follow the examples in the text and then modify the spreadsheet to complete their own bias analyses. Readers without experience using quantitative bias analysis will be able to design, implement, and understand bias analyses that address the major threats to the validity of epidemiologic research. More experienced analysts will value the compilation of bias analysis methods and links to software tools that facilitate their projects.

Timothy L. Lash is a Chair and Professor of Epidemiology at Emory and Matthew P. Fox is a Professor in the Departments of Epidemiology and Global Health at the Boston University School of Public Health. Aliza K. Fink is a Project Manager at Macro International in Bethesda, Maryland. Rich MacLehose is an Associate Professor at the University of Minnesota School of Public Health. Together they have organized and presented many day-long workshops on the methods of quantitative bias analysis. In addition, they have collaborated on many papers that developed methods of quantitative bias analysis or used the methods in the data analysis.

Use the links at the bottom of the page to download the spreadsheets and SAS code from each chapter.

You can download a sample chapter of version 1 here as well.

Copies of the book can be ordered from the publisher's website or from online publishers like Amazon.

Questions, comments or bugs, email: bias.analysis@gmail.com

Have suggestions for ways to improve our spreadsheets and code? Have your own code for bias.analysis? We welcome additions or can link to your site.

Other work by the authors:

You can also find our sensmac SAS macro for dealing with misclassification described in:

 And download the SAS code and sample data set described in:

And our short code examples in SAS and R.

Link to Chapter  4 - Selection Bias Spreadsheet

Link to Chapter  5 - Uncontrolled Confounding Spreadsheet

Link to Chapter  6 - Misclassification Spreadsheet

Link to Chapter  7 - Preparing for QBA: Distributions - R code

Link to Chapters 4-6 - Multidimensional Bias Spreadsheet

Link to Chapter 6 - R code 

Link to Chapter  8 - Summary Probabilistic Bias Analysis - Excel  (uniform, triangular and trapezoidal)

Link to Chapter  8 - Summary Probabilistic Bias Analysis - Excel (beta distributions)

Link to General R code and functions for chapter 8 and 9 code

Link to Chapter  8 - Summary Probabilistic Bias Analysis - R code

Link to Chapter  8 - Summary Probabilistic Bias Analysis - SAS code

Link to Chapter  9 - Record level Probabilistic Bias Analysis - R code

Link to Chapter  9 - Record level Probabilistic Bias Analysis - SAS code

Link to Chapter 11 - Bayes R code

Link to Chapter 11 - JAGS and Stan code