principal stratifictation sensitivity analysis using the HHS method.
Description
Performs a principal stratifictation sensitivity analysis using the method described in Hudgens, Hoering, and Self (2003).
Usage
1 2 3 4 5 
Arguments
z 
vector; contains the grouping values (e.g., treatment assignment) for each record. 
s 
vector; indicates whether a record is selected. 
y 
outcome vector. Can be 
bound 
vector selecting which bound should be calculated, upper and/or lower. Partial string matching is performed. 
selection 
The value of 
groupings 
Vector of two elements 
empty.principal.stratum 
vector of two elements 
ci 
numeric vector; confidence interval level, defaults to 0.95 
ci.method 
character; method by which the confidence interval and
variance are calculated. Can be “analytic” or
“bootstrap”. Defaults to 
na.rm 
logical; indicates whether records that are invalid due to 
N.boot 
integer. Number of bootstrap repetitions that will be run when

oneSidedTest 
logical. Return a one sided confidence interval for ACE. Defaults
to 
twoSidedTest 
logical. Return a two sided confidence interval for ACE. Defaults
to 
isSlaveMode 
logical. Internal Use only. Used in recursion. 
Details
Performs a sensitivity analysis estimating the average causal effect
among those who would have been selected regardless of treatment
assignment (ACE). The method assumes no interference (i.e., potential outcomes of all
subjects are unaffected by treatment assignment of other subjects),
ignorable (i.e., random) treatment
assignment, and monotonicity (i.e., one of the principal strata is
empty). ACE is still not identified after making these assumptions,
so this method computes the lower and upper bounds of the estimated
ACE. These bounds correspond to the values one would get if using
sensitivityGBH
and specifying the sensitivity parameter beta as
Inf
or Inf
.
Value
an object of class sensitivity2d
.
ACE 
ACE=E(Y(g1)Y(g0)S(g1)==S(g0)==selection). Vector of the estimated ACE values at the specified bounds. 
ACE.ci 
vector; confidence interval of ACE determined by
quantiles of bootstrap if 
ACE.var 
vector; estimated variance of ACE. 
y0 
vector of unique 
Fas0 
matrix of estimated empirical distribution function values for

y1 
vector of unique 
Fas1 
matrix of estimated empirical distribution function values for

Author(s)
Bryan E. Shepherd
Department of Biostatistics
Vanderbilt University
Charles Dupont
Department of Biostatistics
Vanderbilt University
References
Hudgens MG, Hoering A, and Self SG (2003), "On the Analysis of Viral Load Endpoints in HIV Vaccien Trials," Statistics in Medicine 22, 22812298.
See Also
sensitivityGBH
, sensitivityJR
, sensitivitySGL
Examples
1 2 3 4 5 6 7 8  data(vaccine.trial)
est.bounds<with(vaccine.trial,
sensitivityHHS(z=treatment, s=hiv.outcome, y=logVL,
selection="infected", groupings=c("placebo","vaccine"),
empty.principal.stratum=c("not infected","infected"),
N.boot=100)
)
est.bounds
