Point and interval estimation of partial attributable risks from case–control data using the R-package ‘pARccs’

The attributable risk (AR) is an epidemiologic measure quantifying the relationship between an exposure factor and a disease at the population level. In addition to its original use as a one-dimensional parameter the AR is increasingly applied in multifactorial epidemiologic situations when the combined impact of multiple exposure factors has to be partitioned into factor-specific components. We discuss the point and interval estimation of the resulting multidimensional parameter termed partial attributable risk (PAR) and introduce the R-package ‘pARccs’, a comprehensive software enabling the application of the methods. ‘pARccs’ allows for point and interval estimation of PAR from case–control data utilizing the non-parametric bootstrap with stratified resampling in combination with the percentile or BCaBCa method to compute confidence intervals. We illustrate the concept of partial attributable risks and the application of the software by an example from a recent case–control study on risk factors for melanoma. We also discuss practical aspects of the software application for epidemiologic purposes and its limitations.