Point and interval estimation of partial attributable risks from casecontrol 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 casecontrol data utilizing the non-parametric bootstrap with stratified resampling in combination with the percentile or BCa BC a 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 casecontrol study on risk factors for melanoma. We also discuss practical aspects of the software application for epidemiologic purposes and its limitations.
bootstrap confidence interval casecontrol study attributable fraction epidemiologic methods sequential attributable risk