bootComb - An R Package to Derive Confidence Intervals for Combinations of Independent Parameter Estimates

Abstract MotivationThe epidemiologist sometimes needs to combine several independent parameter estimates: e.g. (i) adjust an incidence rate for healthcare utilisation, (ii) derive a disease prevalence from the conditional prevalence on another condition and the prevalence of that condition, (iii) adjust a seroprevalence for test sensitivity and specificity. While obtaining the combined parameter estimate is usually straightforward, deriving a corresponding confidence interval often is not. bootComb is an R package using parametric bootstrap sampling to derive such confidence intervals. ImplementationbootComb is a package for the statistical computation environment R. General featuresAs well as a function that returns confidence intervals for parameters combined from several independent estimates, bootComb provides auxiliary functions for 6 common distributions (beta, normal, exponential, gamma, Poisson and negative binomial) to derive best-fit distributions (and their sampling functions) for parameters given their reported confidence intervals. AvailabilitybootComb is available from the Comprehensive R Archive Network (https://CRAN.R-project.org/package=bootComb). Key FeaturesbootComb derives confidence intervals with the required coverage for parameters that are computed from independent parameter estimates for which confidence intervals are reported.Includes auxilliary functions for 6 common distributions (beta, normal, exponential, gamma, Poisson and negative binomial) to derive best-fit distributions (and their sampling functions) for parameters given their reported confidence intervals.R package: open-source, easy-to-use, platform independent.Stable version hosted on CRAN: https://CRAN.R-project.org/package=bootCombLatest development version available from GitHub: https://github.com/gitMarcH/bootComb