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

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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:

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                    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

Note

Visit http://medepi.com for the latest.

Author(s)

Rita Shiau (original author), rita.shiau@sfdph.org; Tomas Aragon, aragon@berkeley.edu, http://www.medepi.com

References

Kenneth J. Rothman and Sander Greenland (1998), Modern Epidemiology, Lippincott-Raven Publishers

Kenneth J. Rothman (2002), Epidemiology: An Introduction, Oxford University Press

See Also

rate2by2.test, oddsratio, riskratio, epitab

Examples

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##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))

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