confIntIndependentProportion: Compute confidence interval for the risk difference of two...

In biostatUZH: Misc Tools of the Department of Biostatistics, EBPI, University of Zurich

Description

Compute confidence interval for the risk difference of two independent samples based on individual Wilson intervals using Newcombe's method.

Usage

confIntIndependentProportion(x, n, conf.level = 0.95)

Arguments

x	Vector with two entries, the successes in the two groups.
n	Vector with two entries, the number of trials.
conf.level	Confidence level for confidence interval.

Value

A list with the entries:

p1	Estimated proportion in first sample.
p2	Estimated proportion in second sample.
d	Estimated difference p_1 - p_2 of proportions.
newcombeCI	Confidence interval for the difference of independent proportions, computed according to Newcombe's method.
waldCI	Wald confidence interval for the difference of independent proportions.

Author(s)

Leonhard Held

References

The Newcombe interval is introduced in

Newcombe, R.G. (1998). Interval estimation for the difference between independent proportions: Comparison of eleven methods. Stat. Med., 17, 873–890.

A worked out example can be found in

Altman, D.G., Machin, D., Bryant, T.N., Gardner, M.J. (2000). Statistics with confidence (p. 49). University Press Belfast.

Examples

# Example from Significance (2010), 7(4), p. 146, "Untimely ripped ?"
n <- c(1515, 108000)
x <- c(0, 100)
confIntIndependentProportion(x, n)

# Fisher p-values
t <- matrix(c(x, n - x), nrow = 2, ncol = 2)
f.t <- fisher.test(t)
f.t$p.value

## Not run: 
# exact test
library("exact2x2")
exact2x2(t, tsmethod = "minlike")$p.value
exact2x2(t, tsmethod = "central")$p.value
exact2x2(t, tsmethod = "blaker")$p.value

## End(Not run)

biostatUZH documentation built on May 2, 2019, 6:06 p.m.