sensitivityHHS: principal stratification sensitivity analysis using the HHS...

In sensitivityPStrat: Principal Stratification Sensitivity Analysis Functions

Description

Performs a principal stratification sensitivity analysis using the method described in Hudgens, Hoering, and Self (2003).

Usage

sensitivityHHS(z, s, y, bound = c("upper", "lower"), selection,
               groupings, empty.principal.stratum, ci = 0.95,
               ci.method = c("bootstrap", "analytic"),
               ci.type = "twoSided", custom.FUN = NULL, na.rm = FALSE,
               N.boot = 100, upperTest = FALSE, lowerTest = FALSE,
               twoSidedTest = TRUE, method = c("ACE", "T1", "T2"),
               isSlaveMode=FALSE)

Arguments

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 NA for unselected records.
bound	vector; which bound should be calculated, “upper” and/or “lower”. Partial string matching is performed.
selection	The value of s indicating selection.
groupings	vector of two elements c(g0,g1); describes to possible group values. The first element g0 being the value of z which delineates the first group, the last element g1 being the value of z which delineates the second group.
empty.principal.stratum	vector of two elements c(s0,s1); describes the s values that select the empty principal stratum. If empty.principal.stratum=c(s0,s1), then stratum defined by S(\var{g0}) = \var{s0} and S(\var{g1}) = \var{s1} is the empty stratum. In this example s0 and s1 refer to the two possible values of s. (Note: method only works if \var{s0} != \var{s1}).
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 c("analytic","bootstrap"). Currently only works for “bootstrap”.
ci.type	character vector; type of confidence interval that the corisponding ci element is referring to. Can be “upper”, “lower”, or “twoSided”. Defaults to "twoSided".
custom.FUN	function; function to calculate custom result. mu0, mu1, p0, p1 are available to be used as arguments in the custom function, where \code{mu0} = E(Y(\var{g0})|S(\var{g0}) = S(\var{g1}) = selected), \code{mu1} = E(Y(\var{g1})|S(\var{g0}) = S(\var{g1}) = selected), \code{p0} = P(S(\var{g0}) = selected), and \code{p1} = P(S(\var{g1}) = selected). The custom function must return a single value.
na.rm	logical; indicates whether records that are invalid due to NA values should be removed from the data set.
N.boot	integer. Number of bootstrap repetitions that will be run when ci.method includes “bootstrap”.
lowerTest	logical. Return the lower one sided p-value for returned tests. Defaults to FALSE
upperTest	logical. Return the upper one sided p-value for returned tests. Defaults to FALSE
twoSidedTest	logical. Return a two sided p-value for returned tests. Defaults to TRUE
method	character vector; type of test statistic calculated. Can be one or more of “ACE”, “T1”, or “T2”. Defaults to "ACE".
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(\var{g1})-Y(\var{g0})|S(\var{g1})=S(\var{g0})=\code{selection}). Vector of the estimated ACE values at the specified bounds. Only exists if method includes “ACE”.
ACE.ci	vector; confidence interval of ACE determined by quantiles of bootstrap if ci.method includes “bootstrap”. Otherwise calculated using analytic variance with large sample normal approximation (NOT YET WORKING). Only exists if method includes “ACE”.
ACE.var	vector; estimated variance of ACE. Only exists if method includes “ACE”.
ACE.p	vector; estimated p-value of ACE. Only exists if method includes “ACE”.
Fas0	function; estimator for the empirical distribution function values for y0 in the first group in the always selected principal stratum at the bounds. Pr(Y(\var{g0}) <= \var{y0}|S(\var{g0}) = S(\var{g1}) = \code{selection})
Fas1	function; estimator for the empirical distribution function values for y1 in the second group in the always selected principal stratum at the bounds. Pr(Y(\var{g1}) <= \var{y1}|S(\var{g0}) = S(\var{g1}) = \code{selection})

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 Vaccine Trials,” Statistics in Medicine 22, 2281-2298.

See Also

sensitivityGBH, sensitivityJR, sensitivitySGL

Examples

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

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"),
                     method=c("ACE", "T1", "T2"), N.boot=100,
                     custom.FUN=function(mu0, mu1, ...) mu1 - mu0,
                     upperTest=TRUE, lowerTest=TRUE, twoSidedTest=TRUE)
                )
est.bounds

sensitivityPStrat documentation built on May 2, 2019, 3:37 p.m.