The aim of matchedcc is to provide epidemiologists using R with Stata-like analysis of matched case-control data. This package has two functions, mcc() and mcci(), which are direct analogues of Stata’s own mcc and mcci commands.
Installation
You can install matchedcc from CRAN with:
install.packages("matchedcc")You can also install the development version from GitHub with pak:
# install.packages("pak")
pak::pkg_install("simpar1471/matchedcc")Examples
The functions in matchedcc are easy to use. To demonstrate their use, we will use the mccxmpl dataset, which is included in matchedcc. This dataset has two columns - cases and controls. In this dataset, cases had experienced a heart attack, and cases and controls were matched accordingly. Each column has only 1 or 0 values, which describe whether a case or control encountered our exposure - in this case, drinking >6 cups of coffee per day.
library(matchedcc)
head(matchedcc::mccxmpl)
#> case control
#> 1 1 1
#> 2 1 0
#> 3 0 1
#> 4 0 0
#> 5 1 1
#> 6 1 1Vector input
The mcc() function will take vectors of 1s and 0s and use these to run a matched case control analysis:
mcc(cases = matchedcc::mccxmpl$case, controls = matchedcc::mccxmpl$control)
#> $data
#> Controls
#> Cases Unexposed Exposed Total
#> Unexposed 8 8 16
#> Exposed 3 8 11
#> Total 11 16 27
#>
#> $mcnemar_chi2
#>
#> McNemar's Chi-squared test
#>
#> data: mcc_table
#> McNemar's chi-squared = 2.2727, df = 1, p-value = 0.1317
#>
#>
#> $mcnemar_exact_p
#> Exact McNemar significance probability
#> 0.2265625
#>
#> $proportions
#> Proportion with factor
#> Cases Controls
#> 0.5925926 0.4074074
#>
#> $statistics
#> estimate [95% CI]
#> statistic estimate lower upper
#> difference 0.1851852 -0.08225420 0.4526246
#> ratio 1.4545455 0.89110096 2.3742568
#> rel. diff. 0.3125000 -0.02436881 0.6493688
#> odds ratio 2.6666667 0.64003641 15.60640362x2 table input
The mcc() function can also accept a 2x2 table with matched case-control data, provided it is in the following format:
| Cases | Controls | |
|---|---|---|
| Exposed | Unexposed | |
| Exposed | a | b |
| Unexposed | c | d |
mcc_table <- table(matchedcc::mccxmpl$control,
matchedcc::mccxmpl$case)
mcc(table = mcc_table)
#> $data
#> Controls
#> Cases Unexposed Exposed Total
#> Unexposed 8 8 16
#> Exposed 3 8 11
#> Total 11 16 27
#>
#> $mcnemar_chi2
#>
#> McNemar's Chi-squared test
#>
#> data: mcc_table
#> McNemar's chi-squared = 2.2727, df = 1, p-value = 0.1317
#>
#>
#> $mcnemar_exact_p
#> Exact McNemar significance probability
#> 0.2265625
#>
#> $proportions
#> Proportion with factor
#> Cases Controls
#> 0.5925926 0.4074074
#>
#> $statistics
#> estimate [95% CI]
#> statistic estimate lower upper
#> difference 0.1851852 -0.08225420 0.4526246
#> ratio 1.4545455 0.89110096 2.3742568
#> rel. diff. 0.3125000 -0.02436881 0.6493688
#> odds ratio 2.6666667 0.64003641 15.6064036‘Immediate’ input
Last but not least, if you have individual cell counts from a 2x2 table, you can provide them to mcci():
mcci(a = 8, b = 8, c = 3, d = 8)
#> $data
#> Controls
#> Cases Unexposed Exposed Total
#> Unexposed 8 8 16
#> Exposed 3 8 11
#> Total 11 16 27
#>
#> $mcnemar_chi2
#>
#> McNemar's Chi-squared test
#>
#> data: mcc_table
#> McNemar's chi-squared = 2.2727, df = 1, p-value = 0.1317
#>
#>
#> $mcnemar_exact_p
#> Exact McNemar significance probability
#> 0.2265625
#>
#> $proportions
#> Proportion with factor
#> Cases Controls
#> 0.5925926 0.4074074
#>
#> $statistics
#> estimate [95% CI]
#> statistic estimate lower upper
#> difference 0.1851852 -0.08225420 0.4526246
#> ratio 1.4545455 0.89110096 2.3742568
#> rel. diff. 0.3125000 -0.02436881 0.6493688
#> odds ratio 2.6666667 0.64003641 15.6064036Validation against Stata
The package is validated against Stata’s own outputs, using 1000 randomly generated mcci runs from Stata. The code to generate these can be seen in /tests/testdata/run-stata-mcc.R.
Citation
Parker S (2024). matchedcc: Stata-like matched case-control analysis. https://github.com/simpar1471/matchedcc/.