Description Usage Arguments Details Value Author(s) References Examples
Computes the conservative exact confidence intervals of Tchetgen Tchetgen and VanderWeele (2012) for treatment effects on a binary outcome in a twostage randomized experiment with interference
1 
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_{1} 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} 
level 
significance level, i.e., method yields a 1level confidence interval 
Confidence intervals are based on a Hoeffdingtype exponential inequality; see section 4.3.2 of Tchetgen Tchetgen and VanderWeele (2012)
est 
estimated treatment effect from Hudgens and Halloran (2008) 
v 
halfwidth of confidence interval 
lower 
lower limit of confidence interval 
upper 
upper limit of confidence interval 
Joseph Rigdon [email protected]
Hudgens, M.G. and Halloran, M.E. “Toward causal inference with interference.” Journal of the American Statistical Association 2008 103:832842.
Tchetgen Tchetgen, E. and VanderWeele, T.J. “On causal inference in the presence of interference.” Statistical Methods in Medical Research 2012 21:5575.
1 2 3 4 5 6 7 8  #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
TV('OE',assign.ex,data.ex,rep(3,10),rep(6,10),0.05)

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