epitools (version 0.5-9)

rate2by2.test: Comparative tests of independence in rx2 rate tables

Description

Tests for independence where 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 xxact, and normal approximation.

Usage

rate2by2.test(x, y = NULL, rr = 1, 
              rev = c("neither", "rows", "columns", "both"))

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; or a single numeric vector for counts followed by person time at risk

y

vector of person-time at risk; if provided, x must be a vector of disease counts

rr

rate ratio reference value (default is no association)

rev

reverse order of "rows", "colums", "both", or "neither" (default)

Value

x

table that was used in analysis

p.value

p value for test of independence

Details

Tests for independence where 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 xxact, and normal approximation. 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. This function can be used to construct a p value function by testing the MUE to the null hypothesis (rr=1) and alternative hypotheses (rr not equal to 1) to calculate two-side mid-p exact p values. For more detail, see Rothman.

References

Kenneth J. Rothman and Sander Greenland (2008), Modern Epidemiology, Lippincott Williams and Wilkins Publishers Kenneth J. Rothman (2002), Epidemiology: An Introduction, Oxford University Press

See Also

rateratio,

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
rate2by2.test(bc,pyears)
rate2by2.test(dd, rev = "r")
rate2by2.test(matrix(c(15, 41, 19017, 28010),2,2))
rate2by2.test(c(15, 41, 19017, 28010))