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 p-value for returned tests. Defaults
to |
upperTest |
logical. Return the upper one sided p-value for returned tests. Defaults
to |
twoSidedTest |
logical. Return a two sided p-value 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 1-unit 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 1-unit increase in Y(\var{g1}).
Specifying beta
=-Inf
or beta
=Inf
calls
sensitivityHHS
.
T1 and T2 are rank-based 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 p-value of ACE. Only exists if
|
T1 |
vector; Vector of the estimated T1 test statistic for specified
|
T1.p |
vector; estimated p-value of T1. Only exists if
|
T2 |
vector; Vector of the estimated T2 statistic for specified
|
T2.p |
vector; estimated p-value of T2. Only exists if
|
beta |
vector; user-specified 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, 531-541.
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
|
Empty Principle Stratum: S(placebo) = not infected, S(vaccine) = infected
ACE: E(Y(vaccine) - Y(placebo) | S(placebo) = S(vaccine) = infected)
0.00 0.25 0.50 0.75 1.00 1.25 1.50
0.4422777 0.3896437 0.3389877 0.2919282 0.2493324 0.2114175 0.1780977
ACE confidence interval:
By analytic method
ci.probs
2.5% 97.5%
0.00 0.22740332 0.6571521
0.25 0.17241550 0.6068720
0.50 0.11720238 0.5607729
0.75 0.06416697 0.5196894
1.00 0.01504383 0.4836210
1.25 -0.02928454 0.4521196
1.50 -0.06850629 0.4247017
By bootstrap method, N = 100
ci.probs
2.5% 97.5%
0.00 0.24737954 0.7091030
0.25 0.20564718 0.6600439
0.50 0.15511125 0.6111184
0.75 0.09989609 0.5732569
1.00 0.03666264 0.5339183
1.25 -0.01596570 0.4987303
1.50 -0.06142105 0.4676071
Empty Principle Stratum: S(placebo) = not infected, S(vaccine) = infected
ACE: E(Y(vaccine) - Y(placebo) | S(placebo) = S(vaccine) = infected)
-Inf -1.00 -0.75 -0.50 -0.25 0.00
0.94628842 0.63668723 0.59372114 0.54619861 0.49519461 0.44227773
0.25 0.50 0.75 1.00 Inf
0.38964373 0.33898766 0.29192820 0.24933240 -0.06428061
ACE confidence interval:
By bootstrap method, N = 100
ci.probs
2.5% 10% 90% 97.5%
-Inf 0.61522416 0.76014712 1.1438723 1.2262497
-1.00 0.39622486 0.49267677 0.7849033 0.9156972
-0.75 0.35072884 0.45625741 0.7328625 0.8707817
-0.50 0.29937502 0.42145775 0.6932779 0.8174504
-0.25 0.24845589 0.36721054 0.6419684 0.7569787
0.00 0.19985869 0.30878373 0.5997898 0.6947034
0.25 0.15095735 0.25233755 0.5499366 0.6418495
0.50 0.10412916 0.18822557 0.5077163 0.5912298
0.75 0.06098753 0.12937695 0.4709357 0.5443649
1.00 0.01895324 0.07782765 0.4328943 0.5019636
Inf -0.35100939 -0.28106923 0.2037434 0.2644687
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