Description Usage Arguments Details Value Author(s) References See Also Examples
Principal stratification sensitivity analysis with time to event data using the method described by Shepherd, Gilbert, and Lumley (2007).
1 2 3 4 5 6 7 | sensitivitySGL(z, s, d, y, v, beta, tau, time.points, selection, trigger,
groupings, empty.principal.stratum, followup.time,
ci=0.95, ci.method = c("analytic", "bootstrap"),
ci.type="twoSided", custom.FUN = NULL, na.rm = FALSE,
N.boot = 100L, 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. |
d |
vector; indicates whether a post-selection event has occurred. Can
be |
y |
vector; the length of time from selection until event ( |
v |
numeric vector; the length of time from randomization until selection or censoring. |
beta |
vector; values of the sensitivity parameter β. |
tau |
maximum observed follow-up time after selection. Selection weights are constant for t>\code{tau}. |
time.points |
vector; time points, t, at which SCE(\var{t}) will be estimated. |
selection |
The value of |
trigger |
logical; the value of |
groupings |
Vector of two elements |
empty.principal.stratum |
vector of two elements |
followup.time |
numeric value; cut-off point for |
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”. |
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 to by optimize to estimate α. |
lowerTest |
logical; Return the lower one sided p-value for SCE. Defaults
to |
upperTest |
logical; Return the upper one sided p-value for SCE. Defaults
to |
twoSidedTest |
logical; Return a two sided p-value for SCE. 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 “survival causal
effect” among those who would have been selected regardless of
treatment assignment (SCE). 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, monotonicity (i.e., one of the principal strata is
empty), and independent censoring (i.e., time from selection to
event is independent of time from selection until censoring). SCE is
then identified by assuming a value of the sensitivity parameter β,
where exp(β) has an odds ratio interpretation (see help for
sensitivityGBH
). Given selection in one treatment arm, the
probability of selection if in the other treatment arm is assumed to be constant for
for T(\code{z})>\code{tau}.
SCE is computed at user specified time points.
Specifying beta
=-Inf
or
beta
=Inf
estimates the bounds for SCE.
object of class sensitivity2d
SCE |
SCE(\var{t})=Pr(T(\var{g0})<=\var{t}|S(\var{g0})=S(\var{g1})=\code{selection}) - Pr(T(\var{g1})<=\var{t}|S(\var{g0})=S(\var{g1})=\code{selection}).
Array of the estimated SCE at all |
SCE.ci |
array; confidence interval of SCE
determined by |
SCE.var |
array; estimated variance of SCE. Array dimensions the same as
element |
ci.map |
list; mapping of confidence interval to quantile probability. Use
numbers contained within as indices to the |
beta |
vector of user-specified β values |
alphahat |
vector of estimated values of α |
y0 |
vector of unique event times in the first group. |
Fas0 |
matrix of estimated empirical distribution function values for
|
y1 |
vector of unique event times in the second group. |
Fas1 |
matrix of estimated empirical distribution function values for
|
Bryan E. Shepherd
Department of Biostatistics
Vanderbilt University
Charles Dupont
Department of Biostatistics
Vanderbilt University
Shepherd BE, Gilbert PB, Lumley T (2007), “Sensitivity analyses comparing time-to-event outcomes existing only in a subset selected postrandomization,” Journal of the American Statistical Association 102, 573-582.
sensitivityGBH
, sensitivityHHS
, sensitivitySGD
,
Surv
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | data(vaccine.trial)
sens.time<-with(vaccine.trial,
sensitivitySGL(z=treatment, s=hiv.outcome, y=followup.yearsART,
d=ARTinitiation, beta=c(.25, 0,-.25), tau=3,
time.points=c(2,3), selection="infected",
trigger="initiated ART", groupings=c("placebo","vaccine"),
empty.principal.stratum=c("not infected","infected"),
N.boot=50, interval=c(-200,200),
upperTest=TRUE, lowerTest=TRUE, twoSidedTest=TRUE)
)
sens.time
sens.time2<-with(vaccine.trial,
sensitivitySGL(z=treatment, s=hiv.outcome, y=followup.yearsART,
d=ARTinitiation, beta=c(.25, 0,-.25), tau=3,
time.points=c(2,3), selection="infected",
trigger="initiated ART", groupings=c("placebo","vaccine"),
empty.principal.stratum=c("not infected","infected"),
custom.FUN=function(Fas0,Fas1,time.points,
...) { Fas0(time.points) - Fas1(time.points) },
N.boot=50, interval=c(-200,200))
)
sens.time2
sens.time3<-with(vaccine.trial,
sensitivitySGL(z=treatment, s=hiv.outcome, y=followup.yearsART,
d=ARTinitiation, beta=c(-Inf,.25,0,-.25,Inf),
tau=3, time.points=c(2,3), selection="infected",
trigger="initiated ART", groupings=c("placebo","vaccine"),
empty.principal.stratum=c("not infected","infected"),
custom.FUN=function(Fas0,Fas1,time.points,
...) { Fas0(time.points) - Fas1(time.points) },
N.boot=50, interval=c(-200,200))
)
sens.time3
|
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