AE.CI | R Documentation |
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.
AE.CI(data, level)
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- |
The attributable effects based confidence interval from inverting n+2
hypothesis tests.
tau.hat |
estimated average treatment effect |
lower |
lower bound of confidence interval |
upper |
upper bound of confidence interval |
Joseph Rigdon jrigdon@wakehealth.edu
Rigdon, J.R. and Hudgens, M.G. (2015). Randomization inference for treatment effects on a binary outcome. Statistics in Medicine, 34(6), 924-935.
ex = matrix(c(8,2,3,7),2,2,byrow=TRUE)
AE.CI(ex,0.05)
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