Data Analytics

Quantitative bias analysis allows to estimate nonrandom errors in epidemiologic studies, assessing the magnitude and direction of biases, and quantifying their uncertainties. Every study has some random error due to its limited sample size, and is susceptible to systematic errors as well, from selection bias to the presence of (un)known confounders or information bias (measurement error, including misclassification). Bias analysis methods were compiled by Lash et al. in their book Applying Quantitative Bias Analysis to Epidemiologic Data. This Shiny app implements bias analyses from the book, as well as others (e.g. by S. Greenland), as computed by the R package episensr . More can be found in the episensr package available for download on R CRAN . The four tabs allow to perform (1) a simple analysis (for bias analysis requiring a 2-by-2 table as data input), (2) an analysis for covariate misclassification (requiring two 2-by-2 tables as data input), (3) a simple analysis with no observed data (for bias analysis that does not have as input an observed 2-by-2 table), and (4) a probabilistic bias analysis.