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 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)

### 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 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

### 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

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))


[Package epitools version 0.5-10.1 Index]