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bootComb—an R package to derive confidence intervals for combinations of independent parameter estimates

Henrion, Marc (2021) 'bootComb—an R package to derive confidence intervals for combinations of independent parameter estimates'. International Journal of Epidemiology, Vol 50, Issue 4, pp. 1071-1076.

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Motivation: To address the limits of facility- or study-based estimates, multiple independent parameter estimates may need to be combined. Specific examples include (i) adjusting an incidence rate for healthcare utilisation, (ii) deriving a disease prevalence from a conditional prevalence and the prevalence of the underlying condition, (iii) adjusting a seroprevalence for test sensitivity and specificity. Calculating combined parameter estimates is generally straightforward, but deriving corresponding confidence intervals often is not. bootComb is an R package using parametric bootstrap sampling to derive such intervals.
Implementation: bootComb is a package for the statistical computation environment R.
General features: Apart from a function returning 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 for parameters given their reported confidence intervals.

Item Type: Article
Subjects: W General Medicine. Health Professions > W 26.5 Informatics. Health informatics
WA Public Health > Statistics. Surveys > WA 900 Public health statistics
Faculty: Department: Clinical Sciences & International Health > Malawi-Liverpool-Wellcome Programme (MLW)
Digital Object Identifer (DOI):
Depositing User: Julie Franco
Date Deposited: 08 Jun 2021 11:19
Last Modified: 10 Dec 2021 13:47


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