exactCI: Exact confidence intervals for treatment effects on a binary...

In interferenceCI: Exact Confidence Intervals in the Presence of Interference

Description

Finds exact confidence intervals for treatment effects on a binary outcome in a two-stage randomized experiment with interference. See Section 4.2 of Rigdon and Hudgens (2014) for details.

Usage

exactCI(eff, g, data, m.a0, m.a1, B2, C2, level)

Arguments

eff	treatment effect of interest; either “DEa0”, “DEa1”, “IE”, “TE”, or “OE”
g	1st stage of randomization vector where element i=1,…,k is equal to 1 if group i was randomized to strategy α_{1} and 0 if randomized to strategy α_{0}
data	2 \times 2\times k array of 2 \times 2 table data where row 1 is treatment=yes, row 2 is treatment=no, column 1 is outcome=yes, and column 2 is outcome=no
m.a0	α_{0} randomization vector where element i=1,…,k is equal to the number of subjects in group i who would receive treatment if group i was randomized to strategy α_{0}
m.a1	α_{0} randomization vector where element i=1,…,k is equal to the number of subjects in group i who would receive treatment if group i was randomized to strategy α_{1}
B2	number of sharp nulls to test in the targeted sampling algorithm
C2	number of re-randomizations (experiments) to conduct in computing the null distribution of the estimator
level	significance level of hypothesis tests, i.e., method yields a 1-level confidence interval

Details

See Section 4.2 of Rigdon and Hudgens (2014) for detailed description. Please plot the p-values against the effect as a check of targeted sampling algorithm performance.

Value

B1	total number of hypotheses that could be tested
C1	total number of re-randomizations (experiments) that could be performed
frac.NA	fraction of hypothesized sharp nulls that are not tested
prob1	final value of targeting parameter q_{p_{l}} in finding lower confidence limit
prob2	final value of targeting parameter q_{p_{u}} in finding upper confidence limit
effect	vector of sharp null hypotheses
p	vector of p-values corresponding to the sharp null hypotheses
lower	lower limit to confidence interval
upper	upper limit to confidence interval

Author(s)

Joseph Rigdon [e-mail]

References

Rigdon, J. and Hudgens, M.G. “Exact confidence intervals in the presence of interference.” Submitted to Statistics and Probability Letters 2014.

Examples

#Made up example with 10 groups of 10 where half are randomized to a0 and half to a1
#a0 is assign 3 of 10 to treatment and half to a1 is assign 6 of 10 to treatment
d = c(1,1,5,3,0,6,3,1,0,4,3,3,0,5,3,2,1,1,5,3,2,2,4,2,1,5,2,2,2,3,4,1,1,1,5,3,1,5,2,2)
data.ex = array(d,c(2,2,10))
assign.ex = c(1,0,0,0,1,1,0,1,1,0)

#Inference for overall effect
l1 = exactCI('OE',assign.ex,data.ex,rep(3,10),rep(6,10),100,100,0.05)

#Check algorithm using a plot
plot(l1$effect,l1$p)

interferenceCI documentation built on May 2, 2019, 3:32 p.m.