| rate2by2.test {epitools} | R Documentation |
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 xxact, and normal approximation.
rate2by2.test(x, y = NULL, rr = 1,
rev = c("neither", "rows", "columns", "both"))
x |
input data can be one of the following: r x 2 table where first column contains disease counts and second column contains person time at risk; or a single numeric vector for counts followed by person time at risk |
y |
vector of person-time at risk; if provided, x must be a vector of disease counts |
rr |
rate ratio reference value (default is no association) |
rev |
reverse order of "rows", "colums", "both", or "neither" (default) |
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 xxact, and normal approximation.
This function expects the following table struture:
counts person-time
exposed=0 (ref) n00 t01
exposed=1 n10 t11
exposed=2 n20 t21
exposed=3 n30 t31
The reason for this is because each level of exposure is compared to
the reference level.
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 method and normal approximation.
x |
table that was used in analysis |
p.value |
p value for test of independence |
Visit http://www.epitools.net for the latest.
Tomas Aragon, aragon@berkeley.edu, http://www.medepi.net/aragon
Kenneth J. Rothman and Sander Greenland (1998), Modern Epidemiology, Lippincott-Raven Publishers
Kenneth J. Rothman (2002), Epidemiology: An Introduction, Oxford University Press
##Examples from Rothman 1998, p. 238
bc <- c(Unexposed = 15, Exposed = 41)
pyears <- c(Unexposed = 19017, Exposed = 28010)
dd <- matrix(c(41,15,28010,19017),2,2)
dimnames(dd) <- list(Exposure=c("Yes","No"), Outcome=c("BC","PYears"))
##midp
rate2by2.test(bc,pyears)
rate2by2.test(dd, rev = "r")
rate2by2.test(matrix(c(15, 41, 19017, 28010),2,2))
rate2by2.test(c(15, 41, 19017, 28010))