rateratio {epitools}  R Documentation 
Calculates rate ratio by medianunbiased estimation (midp), and unconditional maximum likelihood estimation (Wald). Confidence intervals are calculated using exact methods (midp), and normal approximation (Wald).
rateratio(x, y = NULL,
method = c("midp", "wald"),
conf.level = 0.95,
rev = c("neither", "rows", "columns", "both"),
verbose = FALSE)
rateratio.midp(x, y = NULL,
conf.level = 0.95,
rev = c("neither", "rows", "columns", "both"),
verbose = FALSE)
rateratio.wald(x, y = NULL,
conf.level = 0.95,
rev = c("neither", "rows", "columns", "both"),
verbose = FALSE)
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; a single numeric vector of counts followed by
person time at risk; a single numeric vector of counts combined with

y 
numeric vector of persontime at risk; if provided, 
method 
method for calculating rate ratio and confidence interval 
conf.level 
confidence level (default is 0.95) 
rev 
reverse order of "rows", "colums", "both", or "neither" (default) 
verbose 
set to TRUE to return more detailed results (default is FALSE) 
Calculates rate ratio by medianunbiased estimation (midp), and unconditional maximum likelihood estimation (Wald). Confidence intervals are calculated using exact methods (midp), and normal approximation (Wald).
This function expects the following table struture:
counts persontime 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 twosided p values are calculated using midp exact method and normal approximation (Wald).
x 
table that was used in analysis (verbose = TRUE) 
data 
same table as 
measure 
rate ratio and confidence interval 
conf.level 
confidence level used (verbose = TRUE) 
p.value 
p value for test of independence 
Rita Shiau (original author), rita.shiau@sfdph.org; Tomas Aragon, aragon@berkeley.edu, http://www.phdata.science; Adam Omidpanah, adam.omidpanah@wsu.edu https://repitools.wordpress.com/
Kenneth J. Rothman, Sander Greenland, and Timothy Lash (2008), Modern Epidemiology, LippincottRaven Publishers
Kenneth J. Rothman (2012), Epidemiology: An Introduction, Oxford University Press
rate2by2.test
, oddsratio
,
riskratio
, epitab
##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
rateratio(bc,pyears)
rateratio(dd, rev = "r")
rateratio(matrix(c(15, 41, 19017, 28010),2,2))
rateratio(c(15, 41, 19017, 28010))
##midp
rateratio.midp(bc,pyears)
rateratio.midp(dd, rev = "r")
rateratio.midp(matrix(c(15, 41, 19017, 28010),2,2))
rateratio.midp(c(15, 41, 19017, 28010))
##wald
rateratio.wald(bc,pyears)
rateratio.wald(dd, rev = "r")
rateratio.wald(matrix(c(15, 41, 19017, 28010),2,2))
rateratio.wald(c(15, 41, 19017, 28010))