BootComb-an R package to derive confidence intervals for combinations of independent parameter estimates
International Journal of Epidemiology
; 50(4):1071-1076, 2021.
Article
in English
| EMBASE | ID: covidwho-1735570
ABSTRACT
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. Availability:
bootComb is available from the Comprehensive R Archive Network (https//CRAN.R-project.org/package=bootComb).
Full text:
Available
Collection:
Databases of international organizations
Database:
EMBASE
Language:
English
Journal:
International Journal of Epidemiology
Year:
2021
Document Type:
Article
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