# 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

 1 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 [email protected]

## References

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

## Examples

  1 2 3 4 5 6 7 8 9 10 11 #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 30, 2017, 6:56 a.m.