Calculates the incremental value of the surrogate outcome information
IV.event(xone, xzero, deltaone, deltazero, sone, szero, t, landmark, number = 40,
transform = FALSE, extrapolate = TRUE, std = FALSE, conf.int = FALSE,
weight.perturb = NULL, type = "np")numeric vector, observed event times for the primary outcome in the treatment group.
numeric vector, observed event times for the primary outcome in the control group.
numeric vector, event/censoring indicators for the primary outcome in the treatment group.
numeric vector, event/censoring indicators for the primary outcome in the control group.
numeric vector, observed event times for the surrogate outcome in the treatment group.
numeric vector, observed event times for the surrogate outcome in the control group.
time of interest for treatment effect.
landmark time of interest, \(t_0\).
number of points for RMST calculation, default is 40.
TRUE or FALSE; indicates whether a transformation should be used, default is FALSE.
TRUE or FALSE; indicates whether local constant extrapolation should be used, default is FALSE.
TRUE or FALSE; indicates whether standard error estimates should be provided, default is FALSE. Estimates are calculated using perturbation-resampling. Two versions are provided: one that takes the standard deviation of the perturbed estimates (denoted as "sd") and one that takes the median absolute deviation (denoted as "mad").
TRUE or FALSE; indicates whether 95% confidence intervals should be provided. Confidence intervals are calculated using the percentiles of perturbed estimates, default is FALSE. If this is TRUE, standard error estimates are automatically provided.
weights used for perturbation resampling.
Type of estimate that should be provided; options are "np" for the nonparametric estimate or "semi" for the semiparametric estimate, default is "np".
A list is returned:
the estimate, \(\hat{\Delta}(t)\), described in delta.estimate documentation.
the estimate, \(\hat{\Delta}_Q(t,t_0)\), described in R.q.event documention.
the estimate, \(\hat{R}_Q(t,t_0)\), described in R.q.event documention.
the estimate, \(\hat{\Delta}_T(t,t_0)\), described in R.t.estimate documention.
the estimate, \(\hat{R}_T(t,t_0)\), described in R.t.estimate documention.
the estimated incremental value of the surrogate outcome information, described above.
the standard error estimate of \(\hat{\Delta}(t)\); if std = TRUE or conf.int = TRUE.
the standard error estimate of \(\hat{\Delta}(t)\) using the median absolute deviation; if std = TRUE or conf.int = TRUE.
the standard error estimate of \(\hat{\Delta}_Q(t,t_0)\); if std = TRUE or conf.int = TRUE.
the standard error estimate of \(\hat{\Delta}_Q(t,t_0)\) using the median absolute deviation; if std = TRUE or conf.int = TRUE.
the standard error estimate of \(\hat{R}_Q(t,t_0)\); if std = TRUE or conf.int = TRUE.
the standard error estimate of \(\hat{R}_Q(t,t_0)\) using the median absolute deviation; if std = TRUE or conf.int = TRUE.
the standard error estimate of \(\hat{\Delta}_T(t,t_0)\); if std = TRUE or conf.int = TRUE.
the standard error estimate of \(\hat{\Delta}_T(t,t_0)\) using the median absolute deviation; if std = TRUE or conf.int = TRUE.
the standard error estimate of \(\hat{R}_T(t,t_0)\); if std = TRUE or conf.int = TRUE.
the standard error estimate of \(\hat{R}_T(t,t_0)\) using the median absolute deviation; if std = TRUE or conf.int = TRUE.
the standard error estimate of the incremental value; if std = TRUE or conf.int = TRUE.
the standard error estimate of the incremental value using the median absolute deviation; if std = TRUE or conf.int = TRUE.
a vector of size 2; the 95% confidence interval for \(\hat{\Delta}(t)\) based on sample quantiles of the perturbed values; if conf.int = TRUE.
a vector of size 2; the 95% confidence interval for \(\hat{\Delta}_Q(t,t_0)\) based on sample quantiles of the perturbed values; if conf.int = TRUE.
a vector of size 2; the 95% confidence interval for \(\hat{R}_Q(t,t_0)\) based on sample quantiles of the perturbed values; if conf.int = TRUE.
a vector of size 2; the 95% confidence interval for \(\hat{\Delta}_T(t,t_0)\) based on sample quantiles of the perturbed values; if conf.int = TRUE.
a vector of size 2; the 95% confidence interval for \(\hat{R}_T(t,t_0)\) based on sample quantiles of the perturbed values; if conf.int = TRUE.
a vector of size 2; the 95% confidence interval for the incremental value based on sample quantiles of the perturbed values; if conf.int = TRUE.
The incremental value of the surrogate outcome information only is quantified as \(IV_S(t,t_0) = R_Q(t,t_0) - R_T(t,t_0)\) where the definition and estimation procedures for \(R_Q(t,t_0)\) and \(R_T(t,t_0)\) are described in the documentation for R.q.event and R.t.estimate, respectively. The estimate of the incremental value is \(\hat{IV}_S(t,t_0) = \hat{R}_Q(t,t_0) - \hat{R}_T(t,t_0)\).
Parast L, Tian L, and Cai T (2020). Assessing the Value of a Censored Surrogate Outcome. Lifetime Data Analysis, 26(2):245-265.
# NOT RUN {
data(ExampleData)
names(ExampleData)
# }
# NOT RUN {
IV.event(xone = ExampleData$x1, xzero = ExampleData$x0, deltaone = ExampleData$delta1,
deltazero = ExampleData$delta0, sone = ExampleData$s1, szero = ExampleData$s0, t = 5,
landmark=2, type = "np")
IV.event(xone = ExampleData$x1, xzero = ExampleData$x0, deltaone = ExampleData$delta1,
deltazero = ExampleData$delta0, sone = ExampleData$s1, szero = ExampleData$s0, t = 5,
landmark=2, type = "np", std = TRUE, conf.int = TRUE)
# }
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