TV: Conservative exact confidence intervals for treatment effects...

Description Usage Arguments Details Value Author(s) References Examples

Description

Computes the conservative exact confidence intervals of Tchetgen Tchetgen and VanderWeele (2012) for treatment effects on a binary outcome in a two-stage randomized experiment with interference

Usage

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TV(eff, g, data, m.a0, m.a1, 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_{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 1-level confidence interval

Details

Confidence intervals are based on a Hoeffding-type exponential inequality; see section 4.3.2 of Tchetgen Tchetgen and VanderWeele (2012)

Value

est

estimated treatment effect from Hudgens and Halloran (2008)

v

half-width of confidence interval

lower

lower limit of confidence interval

upper

upper limit of confidence interval

Author(s)

Joseph Rigdon [email protected]

References

Hudgens, M.G. and Halloran, M.E. “Toward causal inference with interference.” Journal of the American Statistical Association 2008 103:832-842.

Tchetgen Tchetgen, E. and VanderWeele, T.J. “On causal inference in the presence of interference.” Statistical Methods in Medical Research 2012 21:55-75.

Examples

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#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)

interferenceCI documentation built on May 30, 2017, 6:56 a.m.