AE.CI {RI2by2}

Attributable effects based confidence interval for a treatment effect on a binary outcome

Description

Computes the attributable effects based confidence interval for the average treatment effect on a binary outcome in an experiment where m of n individuals are randomized to treatment by design.

Usage

AE.CI(data, level)

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-level)% confidence interval

Details

The attributable effects based confidence interval from inverting n+2 hypothesis tests.

Value

tau.hat

estimated average treatment effect

lower

lower bound of confidence interval

upper

upper bound of confidence interval

Author(s)

Joseph Rigdon [e-mail]

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)
 AE.CI(ex,0.05)