# tab2by2.test: Comparative tests of independence in rx2 contigency tables In epitools: Epidemiology Tools

## Description

Tests for independence where each row of the rx2 table is compared to the exposure reference level and test of independence two-sided p values are calculated using mid-p exact, Fisher's Exact, and the chi-square test.

## Usage

 ```1 2 3``` ```tab2by2.test(x, y = NULL, correction = FALSE, rev = c("neither", "rows", "columns", "both")) ```

## Arguments

 `x` input data can be one of the following: r x 2 table, vector of numbers from a contigency table (will be transformed into r x 2 table in row-wise order), or single factor or character vector that will be combined with `y` into a table. `y` single factor or character vector that will be combined with `x` into a table (default is NULL) `correction` set to TRUE for Yate's continuity correction (default is FALSE) `rev` reverse order of "rows", "colums", "both", or "neither" (default)

## Details

Tests for independence where each row of the rx2 table is compared to the exposure reference level and test of independence two-sided p values are calculated using mid-p exact, Fisher's Exact, and the chi-square test.

This function expects the following table struture:

 ```1 2 3 4 5 6``` ``` disease=0 disease=1 exposed=0 (ref) n00 n01 exposed=1 n10 n11 exposed=2 n20 n21 exposed=3 n30 n31 ```

The reason for this is because each level of exposure is compared to the reference level.

If you are providing a 2x2 table order does not matter:

If the table you want to provide to this function is not in the preferred form, just use the `rev` option to "reverse" the rows, columns, or both. If you are providing categorical variables (factors or character vectors), the first level of the "exposure" variable is treated as the reference. However, you can set the reference of a factor using the `relevel` function.

Likewise, each row of the rx2 table is compared to the exposure reference level and test of independence two-sided p values are calculated using mid-p exact, Fisher's Exact, Monte Carlo simulation, and the chi-square test.

## Value

 `x` table that was used in analysis `p.value` p value for test of independence `correction` logical specifying if continuity correction was used

## Author(s)

Tomas Aragon, [email protected], http://www.phdata.science

## References

Kenneth J. Rothman and Sander Greenland (1998), Modern Epidemiology, Lippincott-Raven Publishers

Kenneth J. Rothman (2002), Epidemiology: An Introduction, Oxford University Press

Nicolas P. Jewell (2004), Statistics for Epidemiology, 1st Edition, 2004, Chapman & Hall, pp. 73-81

`oddsratio`, `riskratio`
 ```1 2 3 4 5 6 7 8``` ```##Case-control study assessing whether exposure to tap water ##is associated with cryptosporidiosis among AIDS patients tapw <- c("Lowest", "Intermediate", "Highest") outc <- c("Case", "Control") dat <- matrix(c(2, 29, 35, 64, 12, 6),3,2,byrow=TRUE) dimnames(dat) <- list("Tap water exposure" = tapw, "Outcome" = outc) tab2by2.test(dat, rev="c") ```