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

In epitools: Epidemiology Tools

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)

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.

Value

x	table that was used in analysis
p.value	p value for test of independence

Author(s)

Tomas Aragon, [e-mail], http://www.phdata.science

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))

Example output

$x
          bc pyears
Unexposed 15  19017
Exposed   41  28010

$p.value
           Outcome
Predictor   midp.exact       wald
  Unexposed         NA         NA
  Exposed   0.03545742 0.03736289

$x
        Outcome
Exposure BC PYears
     No  15  19017
     Yes 41  28010

$p.value
        two-sided
Exposure midp.exact       wald
     No          NA         NA
     Yes 0.03545742 0.03736289

$x
          Outcome
Predictor  Count Person-time
  Exposed1    15       19017
  Exposed2    41       28010

$p.value
          two-sided
Predictor  midp.exact       wald
  Exposed1         NA         NA
  Exposed2 0.03545742 0.03736289

$x
          Outcome
Predictor  Cases Person-time
  Exposed1    15       19017
  Exposed2    41       28010

$p.value
          two-sided
Predictor  midp.exact       wald
  Exposed1         NA         NA
  Exposed2 0.03545742 0.03736289

epitools documentation built on March 26, 2020, 9:14 p.m.