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Vignettes for blandr
Deepankar Datta
2024-06-09
blandr - about the package
blandr is a package to carry out Bland Altman analyses (also
known as a Tukey mean-difference plot) in R. This package was started in
2015 as existing Bland-Altman R functions did not calculate confidence
intervals: my belief is when that drawing Bland-Altman plots, confidence
intervals should be considered the gold-standard. blandr fills
this need!
blandr further generates plotting data to allow easy
generation of charts using ggplot, as well as R’s in-built drawing
capabilities.
This manual functions as a series of vignettes to instruct on how to
best use the functions. Further details can be gleaned for the
auto-generated descriptions for each function. Some functions aren’t
meant to be used directly by the user, but are internal functions for
the package.
Why should I use it?
To get an open-source library that allows replicable Bland-Altman
analyses using R. Further it generates and plots confidence intervals
for Bland-Altman method comparisons: something that other libraries do
not seem to generate.
Installation
You can install blandr from github with:
# install.packages("devtools")
devtools::install_github("deepankardatta/blandr")
Currently the code is hosted at https://github.com/deepankardatta/blandr/ - in future I
hope it will be available on CRAN for easy integration into R
Loading the library
I assume as you’re here you have loaded the library, but just in
case what you need to do is type this in:
library(blandr)
library(ggplot2)
ggplot2 has also been loaded to help with the graphics
demonstrations.
Important notes and limitations
The use of the significance level parameter
The significance parameter is an optional parameter. It defaults to a
level of 0.95. It is useful to understand what this does (& does
not). The significance parameter only impacts in calculating the level
of confidence intervals. It does not interfere with the limits of
agreement being calculated, which reflect the 95% level as descibred by
Bland and Altman.
R plot - why is it still there
The drawing function using R’s built in tools is still in the package
(but not selected by default): this is for those people who do not have
ggplot2. I’ve left it in there as ggplot is an optional package, plus
rplot seems to work faster for those that need it. However ggplot2 gives
you a lot more power and customisation options when drawing charts.
Loading the sample data
The package contains the PEFR data set in the 1986 Lancet paper
written by Bland and Altman. (Bland, J. M., & Altman, D. (1986).
Statistical methods for assessing agreement between two methods of
clinical measurement. The Lancet, 327(8476), 307-310. http://dx.doi.org/10.1016/S0140-6736(86)90837-8) I do
not claim any copyright on the data - this is meant to allow testing of
the function. I encourage future package authors to use the .rda file if
they so wish.
To load the sample Bland-Altman data use the following command:
load(file="Data/bland.altman.PEFR.1986.rda")
load(file="Data/giavarina.2015.rda")
We want to use the Bland-Altman 1986 data which provides comparative
PEFR data as 4 numeric vectors in a data frame: WrightFirst,
WrightSecond, MiniWrightFirst, and MiniWrightSecond. We can calculate
agreement between two methods: WrightFirst and MiniWrightFirst.
Basic use of the Bland-Altman functions
To start using this package, we can use the “blandr.statistics”
function. This is an important function, that most of the other
functions in the package rely upon.
The function relies on 2 mandatory parameters, and an optional
significance level for confidence intervals. The two mandatory
parameters are two lists of numbers. These need to be paired for the
function to work.
You can see the output the function generates below.
blandr.statistics ( bland.altman.PEFR.1986$WrightFirst , bland.altman.PEFR.1986$MiniWrightFirst , sig.level=0.95 )
## Bland-Altman Statistics
## =======================
## t = -0.22524, df = 16, p-value = 0.8246
## alternative hypothesis: true bias is not equal to 0
##
## =======================
## Number of comparisons: 17
## Maximum value for average measures: 654
## Minimum value for average measures: 218.5
## Maximum value for difference in measures: 73
## Minimum value for difference in measures: -81
##
## Bias: -2.117647
## Standard deviation of bias: 38.76513
##
## Standard error of bias: 9.401925
## Standard error for limits of agreement: 16.39491
##
## Bias: -2.117647
## Bias- upper 95% CI: 17.81354
## Bias- lower 95% CI: -22.04884
##
## Upper limit of agreement: 73.86201
## Upper LOA- upper 95% CI: 108.6177
## Upper LOA- lower 95% CI: 39.10636
##
## Lower limit of agreement: -78.0973
## Lower LOA- upper 95% CI: -43.34165
## Lower LOA- lower 95% CI: -112.8529
##
## =======================
## Derived measures:
## Mean of differences/means: -1.158314
## Point estimate of bias as proportion of lowest average: -0.9691749
## Point estimate of bias as proportion of highest average -0.3237992
## Spread of data between lower and upper LoAs: 151.9593
## Bias as proportion of LoA spread: -1.393562
##
## =======================
## Bias:
## -2.117647 ( -22.04884 to 17.81354 )
## ULoA:
## 73.86201 ( 39.10636 to 108.6177 )
## LLoA:
## -78.0973 ( -112.8529 to -43.34165 )
The function allows you to transfer the values to a list. This can be
useful if you want to use your own custom graphing routines.
stats.data <- blandr.statistics ( bland.altman.PEFR.1986$WrightFirst , bland.altman.PEFR.1986$MiniWrightFirst , sig.level=0.95 )
summary(stats.data)
## Length Class Mode
## means 17 -none- numeric
## differences 17 -none- numeric
## method1 17 -none- numeric
## method2 17 -none- numeric
## sig.level 1 -none- numeric
## sig.level.convert.to.z 1 -none- numeric
## bias 1 -none- numeric
## biasUpperCI 1 -none- numeric
## biasLowerCI 1 -none- numeric
## biasStdDev 1 -none- numeric
## biasSEM 1 -none- numeric
## LOA_SEM 1 -none- numeric
## upperLOA 1 -none- numeric
## upperLOA_upperCI 1 -none- numeric
## upperLOA_lowerCI 1 -none- numeric
## lowerLOA 1 -none- numeric
## lowerLOA_upperCI 1 -none- numeric
## lowerLOA_lowerCI 1 -none- numeric
## proportion 17 -none- numeric
## no.of.observations 1 -none- numeric
## regression.equation 1 -none- character
## regression.fixed.slope 1 -none- numeric
## regression.fixed.intercept 1 -none- numeric
## Bland-Altman Statistics
## =======================
## t = -0.22524, df = 16, p-value = 0.8246
## alternative hypothesis: true bias is not equal to 0
##
## =======================
## Number of comparisons: 17
## Maximum value for average measures: 654
## Minimum value for average measures: 218.5
## Maximum value for difference in measures: 73
## Minimum value for difference in measures: -81
##
## Bias: -2.117647
## Standard deviation of bias: 38.76513
##
## Standard error of bias: 9.401925
## Standard error for limits of agreement: 16.39491
##
## Bias: -2.117647
## Bias- upper 95% CI: 17.81354
## Bias- lower 95% CI: -22.04884
##
## Upper limit of agreement: 73.86201
## Upper LOA- upper 95% CI: 108.6177
## Upper LOA- lower 95% CI: 39.10636
##
## Lower limit of agreement: -78.0973
## Lower LOA- upper 95% CI: -43.34165
## Lower LOA- lower 95% CI: -112.8529
##
## =======================
## Derived measures:
## Mean of differences/means: -1.158314
## Point estimate of bias as proportion of lowest average: -0.9691749
## Point estimate of bias as proportion of highest average -0.3237992
## Spread of data between lower and upper LoAs: 151.9593
## Bias as proportion of LoA spread: -1.393562
##
## =======================
## Bias:
## -2.117647 ( -22.04884 to 17.81354 )
## ULoA:
## 73.86201 ( 39.10636 to 108.6177 )
## LLoA:
## -78.0973 ( -112.8529 to -43.34165 )
You can then take the individual elements of the statistics output by
appending a number in square brackets to the end when calling the list,
e.g.:
## $means
## [1] 503.0 412.5 518.0 431.0 488.0 578.5 388.5 411.0 654.0 439.0 424.5 641.0
## [13] 263.5 477.5 218.5 386.5 439.0
## $differences
## [1] -18 -35 -4 6 -24 -43 49 62 -8 -12 -15 30 7 1 -81 73 -24
## $sig.level
## [1] 0.95
## $sig.level.convert.to.z
## [1] 1.959964
Bland-Altman display
The Bland-Altman display function takes the output of the
‘blandr.statistics’ function, and formats it into a nice form.
blandr.output.text ( bland.altman.PEFR.1986$WrightFirst , bland.altman.PEFR.1986$MiniWrightFirst , sig.level=0.95 )
## Warning in blandr.output.text(bland.altman.PEFR.1986$WrightFirst, bland.altman.PEFR.1986$MiniWrightFirst, : 'blandr.output.text' is deprecated.
## Use 'blandr.output.text' instead.
## See help("Deprecated")
## Number of comparisons: 17
## Maximum value for average measures: 654
## Minimum value for average measures: 218.5
## Maximum value for difference in measures: 73
## Minimum value for difference in measures: -81
##
## Bias: -2.117647
## Standard deviation of bias: 38.76513
##
## Standard error of bias: 9.401925
## Standard error for limits of agreement: 16.39491
##
## Bias: -2.117647
## Bias- upper 95% CI: 17.81354
## Bias- lower 95% CI: -22.04884
##
## Upper limit of agreement: 73.86201
## Upper LOA- upper 95% CI: 108.6177
## Upper LOA- lower 95% CI: 39.10636
##
## Lower limit of agreement: -78.0973
## Lower LOA- upper 95% CI: -43.34165
## Lower LOA- lower 95% CI: -112.8529
##
## Derived measures:
## Mean of differences/means: -1.158314
## Point estimate of bias as proportion of lowest average: -0.9691749
## Point estimate of bias as proportion of highest average -0.3237992
## Spread of data between lower and upper LoAs: 151.9593
## Bias as proportion of LoA spread: -1.393562
##
## Bias:
## -2.117647 ( -22.04884 to 17.81354 )
## ULoA:
## 73.86201 ( 39.10636 to 108.6177 )
## LLoA:
## -78.0973 ( -112.8529 to -43.34165 )
Bland-Altman plots
To draw basic Bland-Altman plots use the blandr.draw
function. The function only has two mandatory parameters.
blandr.draw( bland.altman.PEFR.1986$WrightFirst , bland.altman.PEFR.1986$MiniWrightFirst )
## Warning: Use of `plot.data$x.axis` is discouraged.
## ℹ Use `x.axis` instead.
## Warning: Use of `plot.data$y.axis` is discouraged.
## ℹ Use `y.axis` instead.
As can be seen from above, the display of confidence intervals, and
shading of these intervals is turned on by default. You can turn these
off using the following options.
blandr.draw( bland.altman.PEFR.1986$WrightFirst , bland.altman.PEFR.1986$MiniWrightFirst , ciDisplay = FALSE , ciShading = FALSE )
## Warning: Use of `plot.data$x.axis` is discouraged.
## ℹ Use `x.axis` instead.
## Warning: Use of `plot.data$y.axis` is discouraged.
## ℹ Use `y.axis` instead.
Whilst the vignette only serves as an introduction to the
blandr.draw function, you can read through the R auto-generated
manual to get an idea of the breadth of optional parameters that can be
passed through.
Can I customise other parts of the charts?
One of the most useful uses of the blandr.draw function,
when used in the default ggplot2 mode, is to output and save the raw
ggplot drawing instructions. Further graphics can then be appended in
the usual ggplot manner e.g.:
vignette.chart <- blandr.draw( bland.altman.PEFR.1986$WrightFirst , bland.altman.PEFR.1986$MiniWrightFirst )
vignette.chart
## Warning: Use of `plot.data$x.axis` is discouraged.
## ℹ Use `x.axis` instead.
## Warning: Use of `plot.data$y.axis` is discouraged.
## ℹ Use `y.axis` instead.
vignette.chart <- vignette.chart + geom_vline( xintercept = 300 , linetype = 4 , col=6 )
vignette.chart
## Warning: Use of `plot.data$x.axis` is discouraged.
## ℹ Use `x.axis` instead.
## Use of `plot.data$y.axis` is discouraged.
## ℹ Use `y.axis` instead.
vignette.chart <- vignette.chart + annotate("text", x=400, y=40 , label= "An example of why ggplot2 is a better choice for charting")
vignette.chart
## Warning: Use of `plot.data$x.axis` is discouraged.
## ℹ Use `x.axis` instead.
## Use of `plot.data$y.axis` is discouraged.
## ℹ Use `y.axis` instead.
Another method of creating custom charts is to call all the functions
separately, and then plotting however you want with the data, e.g.,
wright.stats <- blandr.statistics( bland.altman.PEFR.1986$WrightFirst , bland.altman.PEFR.1986$MiniWrightFirst )
wright.plot <- blandr.plot.limits (wright.stats )
plot( x=wright.stats$means , y=wright.stats$differences )
Imagination and creativity is the only thing you need now!
I hate the plot limits!
When the blandr.draw function is called, it passes the data
first to the blandr.statistics function, then to the
blandr.plot.limits function. The blandr.plot.limits
function attempts to calculate the “optimal” limits for the X- and Y-
axes; this is based on values generated for the confidence intervals of
the limits of agreement. However this might not always be what the user
wishes.
The blandr.plot.limits forces limits on the rplot charts,
but not the ggplot charts. The ggplot function chooses the
limits automatically from the data. However one of the benefits of
ggplot, as noted above, is its customisability: the limits can be added
on later on.
If we consider the default Bland-Altman chart again:
blandr.draw( bland.altman.PEFR.1986$WrightFirst , bland.altman.PEFR.1986$MiniWrightFirst , ciDisplay = FALSE )
## Warning: Use of `plot.data$x.axis` is discouraged.
## ℹ Use `x.axis` instead.
## Warning: Use of `plot.data$y.axis` is discouraged.
## ℹ Use `y.axis` instead.
The confidence intervals are switched off to allow comparison to
figure 2 of Bland and Altman’s 1986 paper. We can see the “calculated
optimal” axes mean that the lowest value on the x-axis is roughly
200.
One method is calling the function, and appending further ggplot
instructions to this.
- This is the original chart, saved as a ggplot object
vignette.chart <- blandr.draw( bland.altman.PEFR.1986$WrightFirst , bland.altman.PEFR.1986$MiniWrightFirst , ciDisplay = FALSE )
vignette.chart
## Warning: Use of `plot.data$x.axis` is discouraged.
## ℹ Use `x.axis` instead.
## Warning: Use of `plot.data$y.axis` is discouraged.
## ℹ Use `y.axis` instead.
- We can then change the chart to use the calculated plot limits. (In
reality you can change this to any limits you want)
wright.stats <- blandr.statistics( bland.altman.PEFR.1986$WrightFirst , bland.altman.PEFR.1986$MiniWrightFirst )
wright.plot <- blandr.plot.limits (wright.stats )
vignette.chart.2 <- vignette.chart + ggplot2::coord_cartesian( xlim=c( wright.plot$x_lower , wright.plot$x_upper ) , ylim=c( wright.plot$y_lower , wright.plot$y_upper ) )
vignette.chart.2
## Warning: Use of `plot.data$x.axis` is discouraged.
## ℹ Use `x.axis` instead.
## Warning: Use of `plot.data$y.axis` is discouraged.
## ℹ Use `y.axis` instead.
- Or we can force the lower x-boundary to be 0, to match the
Bland-Altman 1986 paper, figure 2.
vignette.chart.3 <- vignette.chart + ggplot2::coord_cartesian( xlim=c( 0 , wright.plot$x_upper ) , ylim=c( wright.plot$y_lower , wright.plot$y_upper ) , expand = FALSE )
vignette.chart.3
## Warning: Use of `plot.data$x.axis` is discouraged.
## ℹ Use `x.axis` instead.
## Warning: Use of `plot.data$y.axis` is discouraged.
## ℹ Use `y.axis` instead.
Do note that coord_cartesian has been used as an option,
rather than xlim - see the ggplot2 cheatsheet to understand the
differences between the 2 commands for limiting the chart. I consider
coord_cartesian as the better option.
Further note that I have also declared expand = FALSE: again
see the ggplot2 cheatsheet, and have a play around to understand how it
changes your charts.
The benefits of forcing chart limits means that you can have multiple
charts using the same Y-axis for comparison.
Drawing with R’s built in plotting functions.
You can use R’s built in plotting tools, but adding the switch
plotter=“rplot” as shown below:
blandr.draw( bland.altman.PEFR.1986$WrightFirst , bland.altman.PEFR.1986$MiniWrightFirst , plotter="rplot" )
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.