Description Usage Arguments Details Value Author(s) References See Also Examples
Performs a sensitivity analysis using the method described in Gilbert, Bosch, and Hudgens (2003).
1 2 3 4 5 6 7  sensitivityGBH(z, s, y, beta, selection, groupings,
empty.principal.stratum, ci = 0.95,
ci.method = c("analytic", "bootstrap"),
ci.type = "twoSided", custom.FUN = NULL, na.rm = FALSE,
N.boot = 100, interval = c(100, 100),
upperTest = FALSE, lowerTest = FALSE, twoSidedTest = TRUE,
method = c("ACE", "T1", "T2"), isSlaveMode=FALSE)

z 
vector; contains the grouping values (e.g., treatment assignment) for each record. 
s 
vector; indicates whether a record is selected. 
y 
vector; outcome value. Can be 
beta 
vector; values of the β sensitivity parameter. 
selection 
The value of 
groupings 
vector of two elements 
empty.principal.stratum 
vector of two elements 
ci 
numeric vector; confidence interval level. Defaults to 
ci.method 
character; method by which the confidence interval and variance are
calculated. Can be “analytic” or
“bootstrap”. Defaults to 
ci.type 
character vector; type of confidence interval that the corresponding

custom.FUN 
function; function to calculate custom result. 
na.rm 
logical; indicates whether records that are invalid due to 
N.boot 
integer; number of bootstrap repetitions that will be run when

interval 
numeric vector of length 2. Controls the range limits used
by 
lowerTest 
logical. Return the lower one sided pvalue for returned tests. Defaults
to 
upperTest 
logical. Return the upper one sided pvalue for returned tests. Defaults
to 
twoSidedTest 
logical. Return a two sided pvalue for returned tests. Defaults
to 
method 
character vector; type of test statistic calculated. Can be one or more
of “ACE”, “T1”, or “T2”. Defaults to

isSlaveMode 
logical. Internal Use only. Used in recursion. 
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 identified by assuming a value of the sensitivity parameter beta, where exp(β) has an odds ratio interpretation:
If \code{empty.principal.stratum}=c(S(\var{g0})=not\ selected, S(\var{g1})=selected) then given selected if assigned g0, the odds of being selected if assigned g1 multiplicatively increase exp(β) for every 1unit increase in Y(\var{g0}).
If \code{empty.principal.stratum}=c(S(\var{g0})=selected, S(\var{g1})=not\ selected) then given selected if assigned g1, the odds of being selected if assigned g0 multiplicatively increase exp(β) for every 1unit increase in Y(\var{g1}).
Specifying beta
=Inf
or beta
=Inf
calls
sensitivityHHS
.
T1 and T2 are rankbased analogs of ACE. See <REF TBD>.
an object of class sensitivity2d
.
ACE 
vector;
ACE = E(Y(\var{g1})  Y(\var{g0})S(\var{g1}) = S(\var{g0}) = \code{selection}).
Vector of the estimated ACE values for specified 
ACE.ci 
array; confidence interval of ACE determined by
quantiles of bootstrap if 
ACE.var 
vector; estimated variance of ACE. Only exists if

ACE.p 
vector; estimated pvalue of ACE. Only exists if

T1 
vector; Vector of the estimated T1 test statistic for specified

T1.p 
vector; estimated pvalue of T1. Only exists if

T2 
vector; Vector of the estimated T2 statistic for specified

T2.p 
vector; estimated pvalue of T2. Only exists if

beta 
vector; userspecified β values 
alphahat 
vector; estimated values of α 
Fas0 
function; estimator for the empirical distribution function values for y0 in the first group in the always selected principal stratum. Pr(Y(\var{g0}) <= \var{y0}S(\var{g1}) = \code{selection}; β) 
Fas1 
function; estimator for the empirical distribution function values for y1 in the second group in the always selected principal stratum. Pr(Y(\var{g1}) <= \var{y1}S(\var{g0}) = S(\var{g1}) = \code{selection}; β) 
Bryan E. Shepherd
Department of Biostatistics
Vanderbilt University
Charles Dupont
Department of Biostatistics
Vanderbilt University
Gilbert PB, Bosch RJ, and Hudgens MG (2003), “Sensitivity Analysis for the Assessment of Causal Vaccine Effects of Viral Load in HIV Vaccine Trials,” Biometrics 59, 531541.
sensitivityHHS
, sensitivityJR
, sensitivitySGL
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  data(vaccine.trial)
ans<with(vaccine.trial,
sensitivityGBH(z=treatment,s=hiv.outcome,y=logVL,
beta=c(0,.25,.5,.75,1,1.25,1.5),
selection="infected",
groupings=c("placebo","vaccine"),
empty.principal.stratum=c("not infected","infected"),
N.boot=100)
)
ans
ans<with(vaccine.trial,
sensitivityGBH(z=treatment,s=hiv.outcome,y=logVL,
beta=c(Inf,1,0.75,0.5,0.25,0,.25,.5,.75,1,Inf),
selection="infected",
groupings=c("placebo","vaccine"),
empty.principal.stratum=c("not infected","infected"),
ci.method="bootstrap", ci=c(0.95, 0.9, 0.9),
ci.type=c('twoSided', 'upper', 'lower'),
custom.FUN=function(mu0, mu1, ...) mu1  mu0,
N.boot=100, method=c("ACE", "T1", "T2"),
upperTest=TRUE, lowerTest=TRUE, twoSidedTest=TRUE)
)
ans

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