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

Article scientifique 2020 Anglais

Résumé

Abstract Motivation The 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. Implementation bootComb is a package for the statistical computation environment R. General features As 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. Availability bootComb is available from the Comprehensive R Archive Network ( https://CRAN.R-project.org/package=bootComb ). Key Features bootComb 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=bootComb Latest development version available from GitHub: https://github.com/gitMarcH/bootComb

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Henrion, M. (2020). bootComb - An R Package to Derive Confidence Intervals for Combinations of Independent Parameter Estimates. https://doi.org/10.1101/2020.12.01.20241919

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Auteur(s)

Marc Henrion

Statistiques

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