Sample PEFR comparison data from Bland-Altman (1986)

Description

This is the sample PEFR data set from the 1986 Lancet paper written by Bland and Altman. 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.

Usage

data("bland.altman.PEFR.1986")

Format

A data frame with 17 observations on the following 4 variables.

WrightFirst

a numeric vector

WrightSecond

a numeric vector

MiniWrightFirst

a numeric vector

MiniWrightSecond

a numeric vector

References

Bland, J. M., & Altman, D. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet, 327(8476), 307-310. PMID:2868172. http://dx.doi.org/10.1016/S0140-6736(86)90837-8

Examples

data(bland.altman.PEFR.1986)

Data preparation for method comparison analysis

Description

Prepares the data and runs error checks before the calling function runs whatever method analysis mode is wants.

Usage

blandr.data.preparation(method1, method2, sig.level)

Arguments

Value

method.comparison A data frame of paired values. These have been data checked, and empty rows omitted, from the originally supplied data.

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Examples

# Generates two random measurements
measurement1 <- rnorm(100)
measurement2 <- rnorm(100)

# Calls the function - do note that this function was really
# meant to be called from other functions and not a stand-alone funtion
blandr.data.preparation( measurement1 , measurement2, sig.level=0.95 )

Function to load D'arbela mean velocity of circumferential fibre shortening dataset from internet

Description

Loads the D'arbela mean velocity of circumferential fibre shortening dataset from Martin Bland's website.

Usage

blandr.dataset.fibre()

Value

converted.from.dct A data frame containing the dataset

Note

The function converts the STATA DCT data format into a data frame that R can process.

Author(s)

Deepankar Datta deepankar.datta@gmail.com

References

https://www-users.york.ac.uk/~mb55/datasets/pefr.dct - The D'arbela mean velocity of circumferential fibre shortening dataset from Martin Bland's website

https://www-users.york.ac.uk/~mb55/datasets/datasets.htm - Martin Bland's example data-set webpage

Bland JM, Altman DG. (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet i, 307-310.

Examples

blandr.dataset.fibre()
pefr.data <- blandr.dataset.fibre()

Function to load example data sets

Description

Loads example data sets from the internet.

Usage

blandr.dataset.load(dataset.name)

Arguments

Value

example.dataset A data frame containing the requisite dataset

Note

Dataset 1 ("1","PEFR","pefr") - Bland Altman PEFR dataset (from blandr.dataset.pefr)

Dataset 2 ("2","o2sats","sealey") - Selaey oxygen saturations dataset (from blandr.dataset.o2sats)

Dataset 3 ("3","fibre","darbela") - D'arbela mean velocity of circumferential fibre shortening dataset (from blandr.dataset.fibre)

Dataset 4 ("4","sbp","close") - Close systolic blood pressure dataset (from blandr.dataset.sbp)

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Examples

blandr.dataset.load( "pefr" )
pefr.data <- blandr.dataset.load( "pefr" )

Function to load Bland-Altman oxygen saturation dataset from internet

Description

Loads the Bland-Altman oxygen saturation dataset from Martin Bland's website.

Usage

blandr.dataset.o2sats()

Value

converted.from.dct A data frame containing the dataset

Note

The function converts the STATA DCT data format into a data frame that R can process.

Author(s)

Deepankar Datta deepankar.datta@gmail.com

References

https://www-users.york.ac.uk/~mb55/datasets/sealey.dct - The oxygen saturation dataset from Martin Bland's website

https://www-users.york.ac.uk/~mb55/datasets/datasets.htm - Martin Bland's example data-set webpage

Bland JM, Altman DG. (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet i, 307-310.

Examples

blandr.dataset.o2sats()
pefr.data <- blandr.dataset.o2sats()

Function to load Bland-Altman PEFR dataset from internet

Description

Loads the Bland-Altman PEFR dataset from Martin Bland's website.

Usage

blandr.dataset.pefr()

Value

converted.from.dct A data frame containing the dataset

Note

The function converts the STATA DCT data format into a data frame that R can process.

Author(s)

Deepankar Datta deepankar.datta@gmail.com

References

https://www-users.york.ac.uk/~mb55/datasets/pefr.dct - The PEFR dataset from Martin Bland's website

https://www-users.york.ac.uk/~mb55/datasets/datasets.htm - Martin Bland's example data-set webpage

Bland JM, Altman DG. (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet i, 307-310.

Examples

blandr.dataset.pefr()
pefr.data <- blandr.dataset.pefr()

Function to load Close systolic blood pressure dataset from internet

Description

Loads the Close systolic blood pressure dataset from Martin Bland's website.

Usage

blandr.dataset.sbp()

Value

converted.from.dct A data frame containing the dataset

Note

The function converts the STATA DCT data format into a data frame that R can process.

Author(s)

Deepankar Datta deepankar.datta@gmail.com

References

https://www-users.york.ac.uk/~mb55/datasets/pefr.dct - The Close systolic blood pressure dataset from Martin Bland's website

https://www-users.york.ac.uk/~mb55/datasets/datasets.htm - Martin Bland's example data-set webpage

Bland JM, Altman DG. (1995) Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet, 346, 1085-7.

Examples

blandr.dataset.sbp()
pefr.data <- blandr.dataset.sbp()

Bland-Altman display and draw for R

Description

Stub function: calls both the display and plots functions (in that order). Uses the same parameters as the plot and display functions to allow easy all-in-one use.

This function may be deprecated in future, as you really can use the functions easily separately.

Usage

blandr.display.and.draw(
  method1,
  method2,
  plotter = "ggplot",
  method1name = "Method 1",
  method2name = "Method 2",
  plotTitle = "Bland-Altman plot for comparison of 2 methods",
  sig.level = 0.95,
  annotate = FALSE,
  ciDisplay = TRUE,
  ciShading = FALSE,
  normalLow = FALSE,
  normalHigh = FALSE,
  lowest_y_axis = FALSE,
  highest_y_axis = FALSE,
  point_size = 0.8
)

Arguments

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Examples

# Generates two random measurements
measurement1 <- rnorm(100)
measurement2 <- rnorm(100)

# Generates a plot, with no optional arguments
blandr.display.and.draw( measurement1 , measurement2 )

# Generates a plot, with title
blandr.display.and.draw( measurement1 , measurement2 , plotTitle = 'Bland-Altman example plot' )

(DEPRECATED) Bland-Altman display and plot for R

Description

(DEPRECATED) Re-directs to blandr.display.and.draw

Usage

blandr.display.and.plot(
  method1,
  method2,
  method1name = "Method 1",
  method2name = "Method 2",
  plotTitle = "Bland-Altman plot for comparison of 2 methods",
  sig.level = 0.95,
  annotate = FALSE,
  ciDisplay = TRUE,
  ciShading = FALSE,
  normalLow = FALSE,
  normalHigh = FALSE,
  lowest_y_axis = FALSE,
  highest_y_axis = FALSE,
  point_size = 0.8
)

Arguments

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Bland-Altman drawing function for R

Description

Bland-Altman drawing function. Depends on the blandr.statistics function in the package. Will generate a plot via the standard R plotting functions.

Usage

blandr.draw(
  method1,
  method2,
  method1name = "Method 1",
  method2name = "Method 2",
  plotTitle = "Bland-Altman plot for comparison of 2 methods",
  sig.level = 0.95,
  LoA.mode = 1,
  annotate = FALSE,
  ciDisplay = TRUE,
  ciShading = TRUE,
  normalLow = FALSE,
  normalHigh = FALSE,
  lowest_y_axis = FALSE,
  highest_y_axis = FALSE,
  point_size = 0.8,
  overlapping = FALSE,
  plotter = "ggplot",
  x.plot.mode = "means",
  y.plot.mode = "difference",
  plotProportionalBias = FALSE,
  plotProportionalBias.se = TRUE,
  assume.differences.are.normal = TRUE
)

Arguments

Note

Started 2015-11-14

Last update 2015-11-19

Originally designed for LAVAS and CVLA

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Examples

# Generates two random measurements
measurement1 <- rnorm(100)
measurement2 <- rnorm(100)

# Generates a plot, with no optional arguments
blandr.draw( measurement1 , measurement2 )

# Generates a plot, using the in-built R graphics
blandr.draw( measurement1 , measurement2 , plotter = 'rplot' )

# Generates a plot, with title changed
blandr.draw( measurement1 , measurement2 , plotTitle = 'Bland-Altman example plot' )

# Generates a plot, with title changed, and confidence intervals off
blandr.draw( measurement1 , measurement2 , plotTitle = 'Bland-Altman example plot' ,
ciDisplay = FALSE , ciShading = FALSE )

Bland-Altman method comparison

Description

Everyone likes graphs, lines and T-tests. This uses the data provided to generate simple tests whilst trying to explain why they should be treated with caution in method comparison studies. This is hopefully the first step in getting people to use the Bland-Altman functions as I suspect everyone will try to do these tests anyway.

Usage

blandr.method.comparison(method1, method2, sig.level = 0.95)

Arguments

Author(s)

Deepankar Datta deepankar.datta@gmail.com

References

Based on: (1) 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

Linnet K., Limitations of the paired t-test for evaluation of method comparison data. Clin Chem. 1999 Feb;45(2):314-5. PMID: 9931067

Zaki R, Bulgiba A, Ismail R, Ismail NA. Statistical Methods Used to Test for Agreement of Medical Instruments Measuring Continuous Variables in Method Comparison Studies: A Systematic Review PLoS ONE 2012 7(5): e37908. doi: 10.1371/journal.pone.0037908

Examples

# Generates two random measurements
measurement1 <- rnorm(100)
measurement2 <- rnorm(100)

# Call the function
blandr.method.comparison( measurement1 , measurement2 )

Bland-Altman report generator

Description

Generates a report for the Bland-Altman statistics using rMarkdown and Shiny.

Usage

blandr.output.report(method1, method2)

Arguments

Note

Use the function to generate a report. You can also take the .Rmd file to customise it and create

your own report. Or use rMarkdown to save the contents. I couldn't add this to the function as it's

not allowed in CRAN. On the other hand a full Shiny app would take too long. So this is a stop-gap way

of creating this function. Hopefully I can improve it in the future

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Examples

# NOT RUN
# Generates two random measurements
# measurement1 <- rnorm(100)
# measurement2 <- rnorm(100)

# blandr.output.report( measurement1 , measurement2 )
#
# Use this to manually run the rmarkdown template
# However specify where the template is
# Also define your methods as method1 and method2 exactly
# For a reason I can't fathom (or how the list of parameters is constructed)
# not naming them method1 and method2 makes them invisible to the rMarkdown document
#
# rmarkdown::run( file = "blandr_report_template.Rmd" ,
#      render_args = list( runtime = "shiny" ,
#          params = list( method1 = method1 ,
#               method2 = method2 ) ) )
# END OF NOT RUN

(DEPRECATED) Bland-Altman summary statistics display function

Description

(DEPRECATED) Displays results of Bland-Altman analysis in a nicer text format. Relies on the blandr.statistics function in the package.

Usage

blandr.output.text(method1, method2, sig.level = 0.95)

Arguments

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Examples

# Generates two random measurements
measurement1 <- rnorm(100)
measurement2 <- rnorm(100)

# Displays basic statistics for the two measurements in a readable form
blandr.output.text( measurement1 , measurement2 )

(DEPRECATED) Bland-Altman drawing function for R

Description

(DEPRECATED) Re-directs to blandr.draw.r

Usage

blandr.plot(
  method1,
  method2,
  plotter = "ggplot",
  method1name = "Method 1",
  method2name = "Method 2",
  plotTitle = "Bland-Altman plot for comparison of 2 methods",
  sig.level = 0.95,
  annotate = FALSE,
  ciDisplay = TRUE,
  ciShading = TRUE,
  normalLow = FALSE,
  normalHigh = FALSE,
  lowest_y_axis = FALSE,
  highest_y_axis = FALSE,
  point_size = 0.8
)

Arguments

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Bland-Altman plotting function, using ggplot2

Description

Draws a Bland-Altman plot using data calculated using the other functions, using ggplot2

Usage

blandr.plot.ggplot(
  statistics.results,
  method1name = "Method 1",
  method2name = "Method 2",
  plotTitle = "Bland-Altman plot for comparison of 2 methods",
  ciDisplay = TRUE,
  ciShading = TRUE,
  normalLow = FALSE,
  normalHigh = FALSE,
  overlapping = FALSE,
  x.plot.mode = "means",
  y.plot.mode = "difference",
  plotProportionalBias = FALSE,
  plotProportionalBias.se = TRUE,
  assume.differences.are.normal = TRUE
)

Arguments

Value

ba.plot Returns a ggplot data set that can then be plotted

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Examples

# Generates two random measurements
measurement1 <- rnorm(100)
measurement2 <- rnorm(100)

# Generates a ggplot
# Do note the ggplot function wasn't meant to be used on it's own
# and is generally called via the bland.altman.display.and.draw function

# Passes data to the blandr.statistics function to generate Bland-Altman statistics
statistics.results <- blandr.statistics( measurement1 , measurement2 )

# Generates a ggplot, with no optional arguments
blandr.plot.ggplot( statistics.results )

# Generates a ggplot, with title changed
blandr.plot.ggplot( statistics.results , plotTitle = "Bland-Altman example plot" )

# Generates a ggplot, with title changed, and confidence intervals off
blandr.plot.ggplot( statistics.results , plotTitle = "Bland-Altman example plot" ,
ciDisplay = FALSE , ciShading = FALSE )

Bland-Altman plot limits for R

Description

Works out plot limits for the Bland-Altman plots. Depends on the blandr.statistics function in the package.

Usage

blandr.plot.limits(
  statistics.results,
  lowest_y_axis = FALSE,
  highest_y_axis = FALSE
)

Arguments

Value

x_upper The upper limit of the X-axis

x_lower The lower limit of the X-axis

y_upper The upper limit of the Y-axis

y_lower The lower limit of the Y-axis

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Examples

# Generates two random measurements
measurement1 <- rnorm(100)
measurement2 <- rnorm(100)

# Passes data to the blandr.statistics function to generate Bland-Altman statistics
statistics.results <- blandr.statistics( measurement1 , measurement2 )

# Calls the function
blandr.plot.limits( statistics.results )

Bland-Altman histogram and density plot

Description

Generates a combined histogram and density curve for Bland-Altman differences

Usage

blandr.plot.normality(statistics.results)

Arguments

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Bland-Altman differences QQ plot

Description

Generates a QQ plot for Bland-Altman differences

Usage

blandr.plot.qq(statistics.results)

Arguments

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Bland-Altman plotting function, using basic R drawing functions

Description

Draws a Bland-Altman plot using data calculated using the other functions, using the in-built R graphics

Usage

blandr.plot.rplot(
  statistics.results,
  plot.limits,
  method1name = "Method 1",
  method2name = "Method 2",
  plotTitle = "Bland-Altman plot for comparison of 2 methods",
  annotate = FALSE,
  ciDisplay = TRUE,
  ciShading = TRUE,
  normalLow = FALSE,
  normalHigh = FALSE,
  point_size = 0.8
)

Arguments

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Examples

# Generates two random measurements
measurement1 <- rnorm(100)
measurement2 <- rnorm(100)

# Generates a basic plot
# Do note the blandr.plot.rplot function wasn't meant to be used on it's own
# and is generally called via the bland.altman.display.and.draw function

# Passes data to the blandr.statistics function to generate Bland-Altman statistics
statistics.results <- blandr.statistics( measurement1 , measurement2 )
# Passed data to the blandr.plot.limits function to generate plot limits
plot.limits <- blandr.plot.limits( statistics.results )

# Generates a basic plot, with no optional arguments
blandr.plot.rplot( statistics.results , plot.limits )

# Generates a basic plot, with title changed
blandr.plot.rplot( statistics.results , plot.limits , plotTitle = 'Bland-Altman example plot' )
# Generates a basic plot, with title changed, and confidence intervals off
blandr.plot.rplot( statistics.results , plot.limits , plotTitle = 'Bland-Altman example plot' ,
ciDisplay = FALSE , ciShading = FALSE )

Bland-Altman statistics for R

Description

Bland-Altman analysis function for R. Package created as existing functions don't suit my needs, and don't generate 95\ for bias and limits of agreement. This base function calculates the basic statistics, and generates return values which can be used in the related blandr.display and bland.altamn.plot functions. However the return results can be used to generate a custom chart if desired.

Usage

blandr.statistics(method1, method2, sig.level = 0.95, LoA.mode = 1)

Arguments

Value

An object of class 'blandr' is returned. This is a list with the following elements:

Note

The function will give similar answers when used on the original Bland-Altman PEFR data sets. They won't be exactly the same as (a) for 95\

The function depends on paired values.

It currently only can currently work out fixed bias.

Improvements for the future: proportional bias charts will need further work

Started 2015-11-14

Last update 2016-02-04

Originally designed for LAVAS and CVLA

Author(s)

Deepankar Datta deepankar.datta@gmail.com

References

Based on: (1) 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

Confidence interval work based on follow-up paper: (2) Altman, D. G., & Bland, J. M. (2002). Commentary on quantifying agreement between two methods of measurement. Clinical chemistry, 48(5), 801-802. http://www.clinchem.org/content/48/5/801.full.pdf

Examples

# Generates two random measurements
measurement1 <- rnorm(100)
measurement2 <- rnorm(100)

# Generates Bland-Altman statistics data of the two measurements
blandr.statistics( measurement1 , measurement2 )

Bland-Altman statistics for R - submitting data as a formula

Description

If data is supplied as a formula, the function interprets it and then passes it through to the main blandr.statistics function.

Usage

blandr.statistics.formula(formula, data = parent.frame(), ...)

Arguments

Note

This was initially packaged in the same file as the blandr.statistics function. Changes to R package checks made it easier to separate it out, but functionally it has not changed.

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Examples

# Generates two random measurements
measurement1 <- rnorm(100)
measurement2 <- rnorm(100)

# Generates Bland-Altman statistics data of the two measurements using the formula interface

blandr.statistics.formula( measurement2 ~ measurement1 )

# Example with a real data set
blandr.statistics.formula( Method.B ~ Method.A, data = giavarina.2015 )

Sample comparison data from Giavarina (2015)

Description

This is sample comparison data, taken from Giavarina's 2015 paper on Bland-Altman analysis. The data is from table 1 of the paper. 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.

Usage

data("giavarina.2015")

Format

A data frame with 30 observations on the following 5 variables.

Method.A

a numeric vector

Method.B

a numeric vector

Mean

a numeric vector

Difference

a numeric vector

Diff.Mean.Proportion

a factor with levels

References

Giavarina D. Understanding Bland Altman analysis. Biochemia Medica. 2015;25(2):141-151. doi:10.11613/BM.2015.015.

Examples

data(giavarina.2015)

Bland-Altman Analysis

Description

Bland-Altman Analysis

Usage

jamoviBAanalysis(
  data,
  method1,
  method2,
  ciDisplay = TRUE,
  ciShading = TRUE,
  plotProportionalBias = FALSE,
  plotProportionalBias.se = TRUE,
  overlapping = FALSE
)

Arguments

Value

A results object containing:

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$table$asDF

as.data.frame(results$table)

Differences Histogram and Density Curve

Description

Differences Histogram and Density Curve

Usage

jamoviBAplotHistogram(data, method1, method2)

Arguments

Value

A results object containing:

Differences Q-Q Plot

Description

Differences Q-Q Plot

Usage

jamoviBAplotQQ(data, method1, method2)

Arguments

Value

A results object containing:

Bland-Altman Raw Statistics

Description

Bland-Altman Raw Statistics

Usage

jamoviBAstats(data, method1, method2)

Arguments

Value

A results object containing:

Print method for blandr objects

Description

Compactly print the statistics used to construct a Bland-Altman plot

Usage

## S3 method for class 'blandr'
print(x, digits = getOption("digits"), ...)

Arguments

Author(s)

Deepankar Datta deepankar.datta@gmail.com

Examples

results = blandr.statistics.formula(Method.B ~ Method.A, data = giavarina.2015)
results