twoxtwo

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The twoxtwo package provides a collection of functions to display, summarize, and analyze data in two-by-two contingency tables. Statistical analysis functions are oriented towards epidemiological investigation of exposure/outcome relationships.

Installation

 ## install.packages("devtools")
devtools::install_github("vpnagraj/twoxtwo", build_vignettes = TRUE)

Features

• twoxtwo(): Construct twoxtwo object
• odds_ratio(): Estimate odds ratio and confidence interval
• risk_ratio(): Estimate risk ratio and confidence interval
• risk_diff(): Estimate risk difference and confidence interval
• fisher(): Perform Fisher’s exact test
• chisq(): Perform Pearson’s chi-squared test
• arp(): Estimate attributable risk proportion (ARP) and confidence interval
• parp(): Estimate population attributable risk proportion (PARP) and confidence interval
• ein(): Estimate exposure impact number (EIN) and confidence interval
• cin(): Estimate case impact number (CIN) and confidence interval
• ecin(): Estimate exposed cases impact number (ECIN) and confidence interval
• summary.twoxtwo(): Summarize twoxtwo object
• print.twoxtwo(): Print twoxtwo object
• display(): Render twoxtwo table contents as a knitr::kable

Usage

Example

First load twoxtwo and dplyr to help prep data:

 library(twoxtwo)
 library(dplyr)

Next create a object with S3 class twoxtwo. For this example, use the twoxtwo::titanic dataset. Note that "exposure" and "outcome" variables must each be binary variables:

crew_2x2 <-
 titanic %>%
 twoxtwo(.data = ., exposure = Crew, outcome = Survived)
 
crew_2x2
 # | | |OUTCOME |OUTCOME |
 # |:--------|:----------|:------------|:-----------|
 # | | |Survived=Yes |Survived=No |
 # |EXPOSURE |Crew=TRUE |212 |673 |
 # |EXPOSURE |Crew=FALSE |499 |817 |

The twoxtwo class has its own summary.twoxtwo() method that computes effect measures (odds ratio, risk ratio, and risk difference):

 summary(crew_2x2)
 # 
 # | | |OUTCOME |OUTCOME |
 # |:--------|:----------|:------------|:-----------|
 # | | |Survived=Yes |Survived=No |
 # |EXPOSURE |Crew=TRUE |212 |673 |
 # |EXPOSURE |Crew=FALSE |499 |817 |
 # 
 # 
 # Outcome: Survived
 # Outcome + : Yes
 # Outcome - : No
 # 
 # Exposure: Crew
 # Exposure + : TRUE
 # Exposure - : FALSE
 # 
 # Number of missing observations: 0
 # 
 # Odds Ratio: 0.516 (0.426,0.624)
 # Risk Ratio: 0.632 (0.551,0.724)
 # Risk Difference: -0.14 (-0.178,-0.101)

Individual measures of effect, hypothesis tests, and impact numbers can be calculated using the twoxtwo object. For example:

crew_2x2 %>%
 odds_ratio()
 # # A tibble: 1 x 6
 # measure estimate ci_lower ci_upper exposure outcome 
 # <chr> <dbl> <dbl> <dbl> <chr> <chr> 
 # 1 Odds Ratio 0.516 0.426 0.624 Crew::TRUE/FALSE Survived::Yes/No

crew_2x2 %>%
 chisq()
 # # A tibble: 1 x 9
 # test estimate ci_lower ci_upper statistic df pvalue exposure outcome
 # <chr> <lgl> <lgl> <lgl> <dbl> <int> <dbl> <chr> <chr> 
 # 1 Pearson'... NA NA NA 46.5 1 8.97e-12 Crew::T... Surviv...

Note that data analysis can also be performed without first creating the twoxtwo object:

titanic %>%
 odds_ratio(.data = ., exposure = Crew, outcome = Survived)
 # # A tibble: 1 x 6
 # measure estimate ci_lower ci_upper exposure outcome 
 # <chr> <dbl> <dbl> <dbl> <chr> <chr> 
 # 1 Odds Ratio 0.516 0.426 0.624 Crew::TRUE/FALSE Survived::Yes/No

titanic %>%
 chisq(.data = ., exposure = Crew, outcome = Survived)
 # # A tibble: 1 x 9
 # test estimate ci_lower ci_upper statistic df pvalue exposure outcome
 # <chr> <lgl> <lgl> <lgl> <dbl> <int> <dbl> <chr> <chr> 
 # 1 Pearson'... NA NA NA 46.5 1 8.97e-12 Crew::T... Surviv...

Vignettes

The package includes vignettes to describe usage in more detail.

For details on the twoxtwo data structure and demonstration of basic usage:

 vignette("basic-usage", package = "twoxtwo")

For formulas and examples of how to calculate measures of effect:

 vignette("measures-of-effect", package = "twoxtwo")

For information on hypothesis testing functionality in the package:

 vignette("hypothesis-testing", package = "twoxtwo")

For formulas and demonstration of attributable fraction and impact number calculations:

 vignette("af-impact", package = "twoxtwo")

Contributing

Please use GitHub issues to report bugs or request features. Contributions will be reviewed via pull requests.