A Guide to Implementing Quantitative Bias Analysis

Estimates of association from nonrandomized epidemiologic studies are susceptible to two types of error: random error and systematic error. Random error, or sampling error, is often called chance, and decreases toward zero as the sample size increases and the data are more efficiently distributed in the categories of the adjustment variables. The amount of random error in an estimate of association is measured by its precision. Systematic error, often called bias, does not decrease toward zero as the sample size increases or with more efficient distributions in the categories of the analytic variables. The amount of systematic error in an estimate of association is measured by its validity. Conventional confidence intervals depict the random error about an estimate of association, but give no information about the amount of systematic error. The objective of quantitative bias analysis is to estimate quantitatively the systematic error that remains after implementing a study design and analysis. For comprehensive guidance on study design and analysis, the reader should consult an epidemiology methods textbook, such as Modern Epidemiology (Rothman et al., 2008b ). This text not only assumes that the reader has applied established principles for study design and analysis but also recognizes that the systematic error remaining after implementing those principles merits quantification and presentation. The next sections briefly review principles of design and analysis that have presumably been applied. They are followed by sections on planning for quantitative bias analysis and a brief overview of the types of bias analyses described in this chapter.