rateratio {epitools} | R Documentation |
Rate ratio estimation and confidence intervals
Description
Calculates rate ratio by median-unbiased estimation (mid-p), and unconditional maximum likelihood estimation (Wald). Confidence intervals are calculated using exact methods (mid-p), and normal approximation (Wald).
Usage
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)
Arguments
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 person-time 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) |
Details
Calculates rate ratio by median-unbiased estimation (mid-p), and unconditional maximum likelihood estimation (Wald). Confidence intervals are calculated using exact methods (mid-p), and normal approximation (Wald).
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 (Wald).
Value
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 |
Author(s)
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/
References
Kenneth J. Rothman, Sander Greenland, and Timothy Lash (2008), Modern Epidemiology, Lippincott-Raven Publishers
Kenneth J. Rothman (2012), Epidemiology: An Introduction, Oxford University Press
See Also
rate2by2.test
, oddsratio
,
riskratio
, epitab
Examples
##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))