Relaxation of the parameter independence assumption in the bootComb R package

Abstract

Abstract Background The bootComb R package allows researchers to derive confidence intervals with correct target coverage for arbitrary combinations of arbitrary numbers of independently estimated parameters. Previous versions (<1.1.0) of bootComb used independent bootstrap sampling and required that the parameters themselves are independent—an unrealistic assumption in some real-world applications. Findings Using Gaussian copulas to define the dependence between parameters, the bootComb package has been extended to allow for dependent parameters. Implications The updated bootComb package can now handle cases of dependent parameters, with users specifying a correlation matrix defining the dependence structure. While in practice it may be difficult to know the exact dependence structure between parameters, bootComb allows running sensitivity analyses to assess the impact of parameter dependence on the resulting confidence interval for the combined parameter. Availability bootComb is available from the Comprehensive R Archive Network (https://CRAN.R-project.org/package=bootComb).

Keywords

• biostatistics
• bootstrap
• confidence intervals
• estimation
• R