1.

, which is 0.2855.

2.

, which is 1.75.

3. The

, which is 1.24 to 3.80.

Using this formula, the risk ratio would be expressed as 2.17, 95 percent CI 1.24 to 3.80.

You could also use R to calculate a risk ratio and 95 percent CI for the fourfold table in Figure

13-2 with the following steps:

1. Create a matrix.

Create a matrix called obese_HTN with this code: obese_HTN <- matrix(c(14,12,7,27),nrow = 2,

ncol = 2).

2. Load a library.

For many epidemiologic calculations, you can use the epitools package in R and use a command

from this package to calculate the risk ratio and 95 percent CI. Load the epitools library with this

command: library(epitools).

3. Run the command on the matrix.

In this case, run the riskratio.wald command on the obese_HTN matrix you created in Step 1:

riskratio.wald(obese_HTN).

The output is shown in Listing 13-1.

LISTING 13-1 R output from risk ratio calculation on data from Figure

13-2

> riskratio.wald(obese_HTN)

$data

Outcome

Predictor Disease1 Disease2 Total

Exposed1 14 7 21

Exposed2 12 27 39

Total 26 34 60

$measure

risk ratio with 95% C.I.

Predictor estimate lower upper

Exposed1 1.000000 NA NA

Exposed2 2.076923 1.09512 3.938939

$p.value

two-sided

Predictor midp.exact fisher.exact chi.square

Exposed1 NA NA NA

Exposed2 0.009518722 0.01318013 0.00744125

$correction

[1] FALSE

attr(,”method”)

[1] "Unconditional MLE & normal approximation (Wald) CI"

>