rateratio {epitools} | R Documentation |
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).
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 which would be a numeric vector of corresponding person
time at risk
|
y |
numeric vector of person-time at risk; if provided, x must be
a numeric vector of disease counts
|
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 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 t31The 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).
x |
table that was used in analysis (verbose = TRUE) |
data |
same table as x but with marginal totals |
measure |
rate ratio and confidence interval |
conf.level |
confidence level used (verbose = TRUE) |
p.value |
p value for test of independence |
Visit http://www.epitools.net for the latest.
Rita Shiau (original author), rita.shiau@sfdph.org; Tomas Aragon, aragon@berkeley.edu, http://www.medepi.com
Kenneth J. Rothman and Sander Greenland (1998), Modern Epidemiology, Lippincott-Raven Publishers
Kenneth J. Rothman (2002), 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))