| Perm.CI {RI2by2} | R Documentation |
Permutation test confidence interval for a treatment effect on a binary outcome
Description
Computes permutation-based confidence intervals for the
average treatment effect on a binary outcome in an experiment where
m of n individuals are randomized to treatment by design.
Usage
Perm.CI(data, level, nperm)
Arguments
data |
observed 2 by 2 table in matrix form where row 1 is the treatment assignment Z=1 and column 1 is the binary outcome Y=1 |
level |
significance level of hypothesis tests, i.e., method yields a 100(1- |
nperm |
number of randomizations to perform for each hypothesis test |
Details
The permutation confidence interval results from inverting
O(n^{4}) hypothesis tests where n is the total number of
observations in the observed 2 by 2 table. For each hypothesis test,
if n \choose m is less than or equal to nperm, n \choose m
randomizations are performed, but if n
\choose m is greater than nperm, a random sample with replacement of nperm randomizations
are performed.
Value
tau.hat |
estimated average treatment effect |
lower |
lower bound of confidence interval |
upper |
upper bound of confidence interval |
Author(s)
Joseph Rigdon jrigdon@wakehealth.edu
References
Rigdon, J.R. and Hudgens, M.G. (2015). Randomization inference for treatment effects on a binary outcome. Statistics in Medicine, 34(6), 924-935.
Examples
ex = matrix(c(8,2,3,7),2,2,byrow=TRUE)
Perm.CI(ex,0.05,100)