crs

CRS: Composite Reference Standard in R

Purpose

The functions are written to perform sensitivity and specificity calculations using CRS for any binary outcome (and produce valid confidence intervals).

The main function perform_crs(...) takes in a dataset containing one row per sample and at least three columns that represent the index text, imperfect truth and the resolver. The index test and the imperfect truth are expected to have no missing values, however as per the method, the resolver column may contain missing (NA) values as not every sample is expected to have a resolver result.

Details are available in the paper: Clinical evaluation of the APAS Independence: Automated imaging and interpretation of urine cultures using artificial intelligence with composite reference standard discrepant resolution.

Additional background information on CRS can be found in Hawkins et al. (2001) and Alonzo and Pepe (1999).

Installation

To install and load in R, run:

library(devtools) # see http://cran.r-project.org/web/packages/devtools/README.html
devtools::install_github('tystan/crs')
library(crs)

Help

### see help file to run example
?perform_crs

A PDF manual of the function descriptions is included in the repository here: /inst/doc/crs-help.pdf.

Example usage

You can reproduce the analysis in Brenton et al. (2019). First load one of the dataframes that come with the package: brenton2019.

data(brenton2019) # load data from Brenton et al. (2019)
?brenton2019      # will bring up a description of the data
brenton2019       # have a look at data
## A tibble: 881 x 3
##       A     S     P
##   <int> <int> <int>
## 1     1     1    NA
## 2     1     1    NA
## 3     2     2    NA
## 4     2     2    NA
## 5     2     1     2
## 6     1     1     1
## 7     1     1     1
## 8     1     1    NA
## 9     1     1    NA
##10     1     1     1
## ... with 871 more rows

To run CRS, pass the data (or any dataset in the required form) to the perform_crs() function.

# run CRS analysis on brenton2019 data
perform_crs(
  brenton2019,       # data
  index     =  "A",  # index test
  imperfect =  "S",  # imperfect truth
  resolver  =  "P"   # resolver test (with NAs present)
)

The resulting output:

param	p	var_p	se_p	trans	p_lo	p_up
sens	0.919	0.00031	0.0176	probit	0.879	0.948
spec	0.877	0.00051	0.0226	probit	0.827	0.916

You can also recreate the results of Hawkins et al. (2001) by running the below.

data(hawkins2001) # load data from Hawkins et al. (2001)
hawkins2001       # have a look at data
# run CRS analysis on hawkins2001 data
perform_crs(
  hawkins2001,             # data
  index     =  "index",    # index test column in the data
  imperfect =  "ref",      # imperfect truth column in the data
  resolver  =  "resolve"   # resolver test column in the data (with NAs present)
)

param	p	var_p	se_p	trans	p_lo	p_up
sens	0.975	0.000032	0.00567	probit	0.962	0.984
spec	0.550	0.000922	0.03036	probit	0.490	0.609

Comparison to sensitivity and specificity calculations using the imperfect truth only

CRS can be compared to the index test against the imperfect truth by using the get_sens_spec() function.

For the brenton2019 data:

# the sens+spec estimates using the imperfect truth only
get_sens_spec(
  table(brenton2019[["S"]], brenton2019[["A"]])
)

param	cases	correct	est	lo	up
sens	449	423	0.942	0.917	0.960
spec	432	342	0.792	0.751	0.827

Similarly, for the hawkins2001 data:

# the sens+spec estimates using the imperfect truth only
get_sens_spec(
  table(hawkins2001[["ref"]], hawkins2001[["index"]])
)

param	cases	correct	est	lo	up
sens	2000	1800	0.900	0.886	0.912
spec	1000	400	0.400	0.370	0.431

Referencing this work

Please run the following to get citation information when referencing this code.

citation("crs")