Description Usage Arguments Details Value References See Also Examples
sievePH
implements the semiparametric estimation method of Juraska and Gilbert (2013) for the multivariate mark
specific hazard ratio in the competing risks failure time analysis framework. It employs (i) the semiparametric
method of maximum profile likelihood estimation in the treatmenttoplacebo mark density
ratio model (Qin, 1998) and (ii) the ordinary method of maximum partial likelihood estimation of the overall log hazard ratio in the Cox model.
sievePH
requires that the multivariate mark data are fully observed in all failures.
1  sievePH(eventTime, eventInd, mark, tx)

eventTime 
a numeric vector specifying the observed rightcensored time to the event of interest 
eventInd 
a numeric vector indicating the event of interest (1 if event, 0 if rightcensored) 
mark 
either a numeric vector specifying a univariate continuous mark or a data frame specifying a multivariate continuous mark.
No missing values are permitted for subjects with 
tx 
a numeric vector indicating the treatment group (1 if treatment, 0 if placebo) 
sievePH
considers data from a randomized placebocontrolled treatment efficacy trial with a timetoevent endpoint.
The parameter of interest, the markspecific hazard ratio, is the ratio (treatment/placebo) of the conditional markspecific hazard functions.
It factors as the product of the mark density ratio (treatment/placebo) and the ordinary marginal hazard function ignoring mark data.
The mark density ratio is estimated using the method of Qin (1998), while the marginal hazard ratio is estimated using coxph()
in the survival
package.
Both estimators are consistent and asymptotically normal. The joint asymptotic distribution of the estimators is detailed in Juraska and Gilbert (2013).
An object of class sievePH
which can be processed by
summary.sievePH
to obtain or print a summary of the results. An object of class
sievePH
is a list containing the following components:
DRcoef
: a numeric vector of estimates of coefficients φ in the weight function g(v, φ) in the density ratio model
DRlambda
: an estimate of the Lagrange multiplier in the profile score functions for φ (that arises by profiling out the nuisance parameter)
DRconverged
: a logical value indicating whether the estimation procedure in the density ratio model converged
logHR
: an estimate of the marginal log hazard ratio from coxph()
in the survival
package
cov
: the estimated joint covariance matrix of DRcoef
and logHR
coxphFit
: an object returned by the call of coxph()
nPlaEvents
: the number of events observed in the placebo group
nTxEvents
: the number of events observed in the treatment group
mark
: the input object
tx
: the input object
Juraska, M. and Gilbert, P. B. (2013), Markspecific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy. Biometrics 69(2):328<e2><80><93>337.
Qin, J. (1998), Inferences for casecontrol and semiparametric twosample density ratio models. Biometrika 85, 619<e2><80><93>630.
summary.sievePH
, plot.summary.sievePH
, testIndepTimeMark
and testDensRatioGOF
1 2 3 4 5 6 7 8 9 10 11 12 13 14  n < 500
tx < rep(0:1, each=n/2)
tm < c(rexp(n/2, 0.2), rexp(n/2, 0.2 * exp(0.4)))
cens < runif(n, 0, 15)
eventTime < pmin(tm, cens, 3)
eventInd < as.numeric(tm <= pmin(cens, 3))
mark1 < ifelse(eventInd==1, c(rbeta(n/2, 2, 5), rbeta(n/2, 2, 2)), NA)
mark2 < ifelse(eventInd==1, c(rbeta(n/2, 1, 3), rbeta(n/2, 5, 1)), NA)
# fit a model with a univariate mark
fit < sievePH(eventTime, eventInd, mark1, tx)
# fit a model with a bivariate mark
fit < sievePH(eventTime, eventInd, data.frame(mark1, mark2), tx)

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