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Copy file name to clipboardExpand all lines: R/funnel_plot.R
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#' @title Funnel plots for comparing institutional performance
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#' @description An implementation of funnel plots for indirectly standardised ratios, as described by Spiegelhalter (2005) <https://doi.org/10.1002/sim.1970>.
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#' @description An implementation of funnel plots for indirectly standardised ratios, as described by Spiegelhalter (2005) <https://doi.org/10.1002/sim.1970/>.
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#' There are several parameters for the input, with the assumption that you will want smooth,
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#' overdispersed, funnel control limits. Limits may be inflated for overdispersion based on the methods of DerSimonian & Laird (1986), buy calculating a between unit standard deviation (\eqn{\tau})
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#' and constructing an additive random effects models, originally used for meta-analyses of clinical trials data.
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#' The plot colours deliberately avoid red-amber-green colouring, but you could extract this from the ggplot object and change manually if you like.
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#' Future versions of `funnelplotr` may allow users to change this.
#' @references Spiegelhalter et al. (2012) <doi:10.1111/j.1467-985X.2011.01010.x> Statistical methods for healthcare regulation: rating, screening and surveillance: <doi:10.1111/j.1467-985X.2011.01010.x>
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#' @references NHS Digital (2020) SHMI Methodology v .134\url{https://digital.nhs.uk/data-and-information/publications/clinical-indicators/shmi/current}
#' @references Spiegelhalter et al. (2012) Statistical methods for healthcare regulation: rating, screening and surveillance: \url{https://doi.org/10.1111/j.1467-985X.2011.01010.x}
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#' @references NHS Digital (2020) SHMI Methodology v .134\url{https://digital.nhs.uk/data-and-information/publications/clinical-indicators/shmi/current/}
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#'
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#' @examples
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#' # We will use the 'medpar' dataset from the 'COUNT' package.
Copy file name to clipboardExpand all lines: README.Rmd
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# Funnel Plots for Comparing Institutional Performance <imgsrc="man/figures/logo.png"width="160px"align="right" />
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<!-- badges: start -->
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[](https://www.repostatus.org/#active)
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[](https://www.repostatus.org/#active/)
[](https://codecov.io/gh/nhs-r-community/FunnelPlotR?branch=main)
[](https://codecov.io/gh/nhs-r-community/FunnelPlotR?branch=main/)
__This package is the newer version of the older `CMFunnels` package. Development work will focus on this package from now on.__
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This is an implementation of the funnel plot processes, and overdispersion methods described in:<br>
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[Statistical methods for healthcare regulation: rating, screening and surveillance. Spiegelhalter et al (2012)](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1467-985X.2011.01010.x)<br>
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[Funnel plots for comparing institutional performance. Spiegelhalter (2005)](https://onlinelibrary.wiley.com/doi/10.1002/sim.1970)<br>
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[Handling over-dispersion of performance indicators. Spiegelhalter (2005)](https://qualitysafety.bmj.com/content/14/5/347)<br>
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[Statistical methods for healthcare regulation: rating, screening and surveillance. Spiegelhalter et al (2012)](https://doi.org/10.1111/j.1467-985X.2011.01010.x)<br>
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[Funnel plots for comparing institutional performance. Spiegelhalter (2005)](https://doi.org/10.1002/sim.1970)<br>
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[Handling over-dispersion of performance indicators. Spiegelhalter (2005)](https://dx.doi.org/10.1136/qshc.2005.013755)<br>
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It draws funnel plots using `ggplot2` and allows users to specify whether they want to adjust the funnel plot limits for 'overdispersion.' This adjustment makes the assumption that we are dealing with clusters of values (means) at institutions that are themselves arranged around a global mean. We then have 'within' institution variation and 'between institution' variation. The process assessed the expected variance in our data, and where it is greater than that expected by the Poisson distribution, uses the difference as a scaling factor. It is then used in an additive fashion, after an adjustment for outliers by either Winsorised or truncated (with a default 10% at each end of the distribution.)
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Methods are based on those presented in Spiegelhalter's papers and the Care Quality Commission's Intelligent Monitoring methodology documents, with methods for proportions, ratios of counts and indirectly standardised ratios. There is a also a variant method for standardised ratios, used in the NHS' Summary Hospital Mortality Indicator'<br>
This variant uses a log-transformation and truncation of the distribution for calculating overdispersion, whereas Spiegelhalter's methods use a square-root and Winsorisation.
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Please read the package documentation for more info, at: https://nhs-r-community.github.io/FunnelPlotR/
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Funnel Plot HEX sticker/logo by Paul Chipperfield, check him out at: https://themightychip.com/
Copy file name to clipboardExpand all lines: cran-comments.md
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## Release summary
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### Resubmission - 01/06/2023
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I have updated some of the URLS to the most current DOI for for references, made links all https, included trailing slashes where they resolve. All other links tested and current.
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This is a bug-fix release for the FunnelPlotR package:
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* an error handling function was not performing correctly and preventing multiple selections.
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* A logic step for dealing OD adjustment if there is no OD.
Copy file name to clipboardExpand all lines: vignettes/funnel_plots.Rmd
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Funnel plots are a common tool for comparing organisations or units using proportions or standardised rates. A common use of them is for monitoring mortality at hospitals. This is an introductory post on the subject, that gives a little information about them and how they are constructed. It is deliberately light on theory, focusing on use, some of the theory is referenced for interested readers.
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This post also uses a funnel plot function, for indirectly standardised ratios, that I built as part of my PhD work. The function is based on `ggplot2`[@wickhamGgplot2ElegantGraphics2009], and is available at https://github.com/chrismainey/FunnelPlotR, although it's a work in progress.
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This post also uses a funnel plot function, for indirectly standardised ratios, that I built as part of my PhD work. The function is based on `ggplot2`[@wickhamGgplot2ElegantGraphics2009], and is available at https://github.com/chrismainey/FunnelPlotR/, although it's a work in progress.
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There are different kinds of funnel plot, but this post focuses on the type used to compare standardised mortality and other similarly constructed indicators .
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