Description Usage Arguments Details Value Author(s) References Examples
View source: R/SurrogateOutcome.R
Calculates the incremental value of the surrogate outcome information
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xone |
numeric vector, observed event times for the primary outcome in the treatment group. |
xzero |
numeric vector, observed event times for the primary outcome in the control group. |
deltaone |
numeric vector, event/censoring indicators for the primary outcome in the treatment group. |
deltazero |
numeric vector, event/censoring indicators for the primary outcome in the control group. |
sone |
numeric vector, observed event times for the surrogate outcome in the treatment group. |
szero |
numeric vector, observed event times for the surrogate outcome in the control group. |
t |
time of interest for treatment effect. |
landmark |
landmark time of interest, t_0. |
number |
number of points for RMST calculation, default is 40. |
transform |
TRUE or FALSE; indicates whether a transformation should be used, default is FALSE. |
extrapolate |
TRUE or FALSE; indicates whether local constant extrapolation should be used, default is FALSE. |
std |
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"). |
conf.int |
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. |
weight.perturb |
weights used for perturbation resampling. |
type |
Type of estimate that should be provided; options are "np" for the nonparametric estimate or "semi" for the semiparametric estimate, default is "np". |
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).
A list is returned:
delta |
the estimate, \hat{Δ}(t), described in delta.estimate documentation. |
delta.q |
the estimate, \hat{Δ}_Q(t,t_0), described in R.q.event documention. |
R.q |
the estimate, \hat{R}_Q(t,t_0), described in R.q.event documention. |
delta.t |
the estimate, \hat{Δ}_T(t,t_0), described in R.t.estimate documention. |
R.t |
the estimate, \hat{R}_T(t,t_0), described in R.t.estimate documention. |
IV |
the estimated incremental value of the surrogate outcome information, described above. |
delta.sd |
the standard error estimate of \hat{Δ}(t); if std = TRUE or conf.int = TRUE. |
delta.mad |
the standard error estimate of \hat{Δ}(t) using the median absolute deviation; if std = TRUE or conf.int = TRUE. |
delta.q.sd |
the standard error estimate of \hat{Δ}_Q(t,t_0); if std = TRUE or conf.int = TRUE. |
delta.q.mad |
the standard error estimate of \hat{Δ}_Q(t,t_0) using the median absolute deviation; if std = TRUE or conf.int = TRUE. |
R.q.sd |
the standard error estimate of \hat{R}_Q(t,t_0); if std = TRUE or conf.int = TRUE. |
R.q.mad |
the standard error estimate of \hat{R}_Q(t,t_0) using the median absolute deviation; if std = TRUE or conf.int = TRUE. |
delta.t.sd |
the standard error estimate of \hat{Δ}_T(t,t_0); if std = TRUE or conf.int = TRUE. |
delta.t.mad |
the standard error estimate of \hat{Δ}_T(t,t_0) using the median absolute deviation; if std = TRUE or conf.int = TRUE. |
R.t.sd |
the standard error estimate of \hat{R}_T(t,t_0); if std = TRUE or conf.int = TRUE. |
R.t.mad |
the standard error estimate of \hat{R}_T(t,t_0) using the median absolute deviation; if std = TRUE or conf.int = TRUE. |
IV.sd |
the standard error estimate of the incremental value; if std = TRUE or conf.int = TRUE. |
IV.mad |
the standard error estimate of the incremental value using the median absolute deviation; if std = TRUE or conf.int = TRUE. |
conf.int.delta |
a vector of size 2; the 95% confidence interval for \hat{Δ}(t) based on sample quantiles of the perturbed values; if conf.int = TRUE. |
conf.int.delta.q |
a vector of size 2; the 95% confidence interval for \hat{Δ}_Q(t,t_0) based on sample quantiles of the perturbed values; if conf.int = TRUE. |
conf.int.R.q |
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. |
conf.int.delta.t |
a vector of size 2; the 95% confidence interval for \hat{Δ}_T(t,t_0) based on sample quantiles of the perturbed values; if conf.int = TRUE. |
conf.int.R.t |
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. |
conf.int.IV |
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. |
Layla Parast
Parast L, Tian L, and Cai T (2020). Assessing the Value of a Censored Surrogate Outcome. Lifetime Data Analysis, 26(2):245-265.
1 2 3 4 5 6 7 8 9 10 11 | data(ExampleData)
names(ExampleData)
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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