Description Usage Arguments Details Value Author(s) References See Also Examples
Principal stratification sensitivity analysis relaxing monotonicity as described by Jemiai and Rotnitzky (2005) and implemented by Shepherd, Redman, and Ankerst (2008).
1 2 3 4 5 6 7 | sensitivityJR(z, s, y, beta0, beta1, phi, Pi, psi,
selection, groupings,
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,
verbose=getOption("verbose"), 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 values. Can be |
beta0 |
vector; values of the sensitivity parameter β0 linking outcome in group g0 with selection if assigned group g1. |
beta1 |
vector; values of the sensitivity parameter β1 linking outcome in group g1 with selection if assigned group g0. |
phi, Pi, psi |
vector; sensitivity parameters specifying the joint distribution of
S(\var{g0}), S(\var{g1}). Only one of the three
parameters should be specified. |
selection |
The value of |
groupings |
vector of two elements |
ci |
numeric vector; confidence interval value. 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 optimize to estimate α0 and α1. |
lowerTest |
logical. Return the lower one sided p-value for the ACE. Defaults
to |
upperTest |
logical. Return the upper one sided p-value for the ACE. Defaults
to |
twoSidedTest |
logical. Return a two sided p-value for the ACE. Defaults
to |
verbose |
logical; prints dots when bootstrapping to show that something is happening. Bootstrapping can take a long time. |
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) without assuming monotonicity (i.e., that one of the
principal strata is empty). The method assumes no interference (i.e.,
potential outcomes of all subjects are unaffected by treatment
assignment of other subjects) and ignorable (i.e., random) treatment
assignment. ACE is identified by assuming values for the sensitivity
parameters beta0
, beta1
, and one of the parameters phi
, psi
, or Pi
.
The sensitivity parameters beta0
and beta1
have a log-odds ratio
interpretation (see help for sensitivityGBH
).
Only one of the parameters phi
, psi
, or Pi
should
be specified as all depend on each other. psi
is unrestrained
taking any value on the real line. The other parameters, psi
and Pi
have constraints and there will be estimation problems
if these parameters are set at values outside the of their range of
acceptable values based on the observed data. See Shepherd, Gilbert,
Dupont (in press) for more details.
object of class sensitivity3d
ACE |
array; estimated values of ACE for all combinations of |
ACE.ci |
array; confidence interval determined by |
ACE.var |
array; estimated variance of ACE. Array dimensions the same as |
ACE.p |
vector; estimated p-value of ACE. |
beta0 |
vector; β values used for the first group. |
alphahat0 |
vector; estimated α values for the first group. |
Fas0 |
function; estimator for the distribution function of y0 in the first group in the always selected stratum. |
beta1 |
vector; β values used for the second group. |
alphahat1 |
vector; estimated α values for the second group. |
Fas1 |
function; estimator for the distribution function of y1 in the second group in the always selected stratum. |
phi |
vector; phi values used. |
Pi |
vector; Pi values used. |
psi |
vector; psi values used. |
ci.map |
list; mapping of confidence interval to quantile probability. Use
numbers contained within as indices to the |
Bryan E. Shepherd
Department of Biostatistics
Vanderbilt University
Charles Dupont
Department of Biostatistics
Vanderbilt University
Jemiai Y (2005), “Semiparametric Methods for Inferring Treatment Effects on Outcomes Defined Only if a Post-Randomization Event Occurs,” unpublished doctoral dissertation under the supervision of A. Rotnitzky, Harvard School of Public Health, Dept. of Biostatistics.
Shepherd BE, Redman MW, Ankerst DP (2008), “Does Finasteride affect the severity of prostate cancer? A causal sensitivity analysis,” Journal of the American Statistical Association 2008, 484, 1392-1404.
Shepherd BE, Gilbert PB, and Dupont CT, “Sensitivity analyses comparing time-to-event outcomes only existing in a subset selected postrandomization and relaxing monotonicity,” Biometrics, in press.
sensitivityGBH
,
sensitivitySGD
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | data(vaccine.trial)
ansJR<-with(vaccine.trial,
sensitivityJR(z=treatment,s=hiv.outcome,y=logVL,
beta0=c(-1,-.5,0,.5,1),
beta1=c(-1,-.5,0,.5,1),
phi=c(0.95,0.9), selection="infected",
groupings=c("placebo","vaccine"),
N.boot=100)
)
ansJR
data(vaccine.trial)
ansJR<-with(vaccine.trial,
sensitivityJR(z=treatment,s=hiv.outcome,y=logVL,
beta0=c(-1,-.5,0,.5,1),
beta1=c(-1,-.5,0,.5,1),
phi=c(0.95,0.9), selection="infected",
groupings=c("placebo","vaccine"),
custom.FUN=function(mu0, mu1, ...) mu1 - mu0,
upperTest=TRUE, lowerTest=TRUE, twoSidedTest=TRUE,
N.boot=100)
)
ansJR
|
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