RelRisk {DescTools}  R Documentation 
Relative Risk
Description
Computes the relative risk and its confidence intervals.
Confidence intervals are calculated using normal approximation ("wald"
), ("score"
) or by
using odds ratio ("use.or"
)
Usage
RelRisk(x, y = NULL, conf.level = NA,
method = c("score", "wald", "use.or"), delta = 0.5, ...)
Arguments
x 
a numeric vector or a 2x2 numeric matrix, resp. table. 
y 

conf.level 
confidence level. Default is 
method 
method for calculating the relative risk and the confidence intervals. Can be one out of

delta 
small constant to be added to the numerator for calculating the log risk ratio (Wald method). Usual choice is 0.5 although there does not seem to be any theory behind this. (Dewey, M. 2006) 
... 
further arguments are passed to the function 
Details
Best is to always put the outcome variable (disease yes/no) in the columns and the exposure variable in the rows. In other words, put the dependent variable – the one that describes the problem under study – in the columns. And put the independent variable – the factor assumed to cause the problem – in the rows. (Gerritsen, 2010)
According to this, the function expects the following table structure:
diseased=1 diseased=0 exposed=1 (ref) n00 n01 exposed=0 n10 n11
The relative risk is then calculated as:
(exposed & diseased) / exposed rr =  (unexposed & diseased) / unexposed
If the table to be used is not in the
required shape, use the function Rev()
and/or t()
to reverse rows, columns, or both, resp. to transpose the table.
Value
If conf.level
is not NA
then the result will be
a vector with 3 elements for estimate, lower confidence intervall and upper for the upper one.
Else the relative risk will be reported as a single value.
Author(s)
Andri Signorell <andri@signorell.net>, based on code of Yongyi Min and Michael Dewey
References
Rothman, K. J. and Greenland, S. (1998) Modern Epidemiology. LippincottRaven Publishers
Rothman, K. J. (2002) Epidemiology: An Introduction. Oxford University Press
Jewell, N. P. (2004) Statistics for Epidemiology. 1st Edition, 2004, Chapman & Hall, pp. 7381
Selvin, S. (1998) Modern Applied Biostatistical Methods Using SPlus. 1st Edition, Oxford University Press
Gerritsen, A (2010) https://www.theanalysisfactor.com/crosstabulationincohortandcasecontrolstudies/
See Also
Examples
m < matrix(c(78,50,1422,950),
nrow=2,
dimnames = list(water=c("cont", "clean"),
diarrhea=c("yes", "no")))
RelRisk(m, conf.level = 0.95)
mm < cbind(c(9,20),c(41,29))
mm
RelRisk(t(mm), conf.level=0.95)
RelRisk(t(mm), conf.level=0.95, method="wald")
RelRisk(t(mm), conf.level=0.95, method="use.or")