R.multiple.surv: Calculates the proportion of treatment effect explained by...

In laylaparast/Rsurrogate: Robust Estimation of the Proportion of Treatment Effect Explained by Surrogate Marker Information

Calculates the proportion of treatment effect explained by multiple surrogate markers measured at a specified time and primary outcome information up to that specified time

Description

This function calculates the proportion of treatment effect on the primary outcome explained by multiple surrogate markers measured at t_0 and primary outcome information up to t_0. The user can also request a variance estimate, estimated using perturbating-resampling, and a 95% confidence interval. If a confidence interval is requested three versions are provided: a normal approximation based interval, a quantile based interval and Fieller's confidence interval, all using perturbation-resampling. The user can also request an estimate of the incremental value of the surrogate marker information.

Usage

R.multiple.surv(xone, xzero, deltaone, deltazero, sone, szero, type =1, t, 
weight.perturb = NULL, landmark, extrapolate = FALSE, transform = FALSE, 
conf.int = FALSE, var = FALSE, incremental.value = FALSE, approx = T)

Arguments

xone	numeric vector, the observed event times in the treatment group, X = min(T,C) where T is the time of the primary outcome and C is the censoring time.
xzero	numeric vector, the observed event times in the control group, X = min(T,C) where T is the time of the primary outcome and C is the censoring time.
deltaone	numeric vector, the event indicators for the treatment group, D = I(T<C) where T is the time of the primary outcome and C is the censoring time.
deltazero	numeric vector, the event indicators for the control group, D = I(T<C) where T is the time of the primary outcome and C is the censoring time.
sone	matrix of numeric values; surrogate marker measurements at t_0 for treated observations. If X_{1i}<t_0, then the surrogate marker measurements should be NA.
szero	matrix of numeric values; surrogate marker measurements at t_0 for control observations. If X_{0i}<t_0, then the surrogate marker measurements should be NA.
type	type of estimate; options are 1 = two-stage robust estimator, 2 = weighted two-stage robust estimator, 3 = double-robust estimator, 4 = two-stage model-based estimator, 5 = weighted estimator, 6 = double-robust model-bsed estimator; default is 1.
t	the time of interest.
weight.perturb	weights used for perturbation resampling.
landmark	the landmark time t_0 or time of surrogate marker measurement.
extrapolate	TRUE or FALSE; indicates whether the user wants to use extrapolation.
transform	TRUE or FALSE; indicates whether the user wants to use a transformation for the surrogate marker pseudo-score.
conf.int	TRUE or FALSE; indicates whether a 95% confidence interval for delta is requested, default is FALSE.
var	TRUE or FALSE; indicates whether a variance estimate is requested, default is FALSE.
incremental.value	TRUE or FALSE; indicates whether the user would like to see the incremental value of the surrogate marker information, default is FALSE.
approx	TRUE or FALSE indicating whether an approximation should be used when calculating the probability of censoring; most relevant in settings where the survival time of interest for the primary outcome is greater than the last observed event but before the last censored case, default is TRUE.

Details

Details can be found in Parast, L., Cai, T., Tian, L. (2020+). Evaluating Multiple Surrogate Markers with Censored Data. Under Review.

Please email [e-mail] if you would like a copy of this article.

Value

A list is returned:

delta	the estimate, \hat{Δ}(t), described in delta.estimate documentation.
delta.s	the residual treatment effect estimate, \hat{Δ}_S(t,t_0).
R.s	the estimated proportion of treatment effect explained by the set of markers, \hat{R}_S(t,t_0).
delta.var	the variance estimate of \hat{Δ}(t); if var = TRUE or conf.int = TRUE.
delta.s.var	the variance estimate of \hat{Δ}_S(t,t_0); if var = TRUE or conf.int = TRUE.
R.s.var	the variance estimate of \hat{R}_S(t,t_0); if var = TRUE or conf.int = TRUE.
conf.int.normal.delta	a vector of size 2; the 95% confidence interval for \hat{Δ}(t) based on a normal approximation; if conf.int = TRUE.
conf.int.quantile.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.normal.delta.s	a vector of size 2; the 95% confidence interval for \hat{Δ}_S(t,t_0) based on a normal approximation; if conf.int = TRUE.
conf.int.quantile.delta.s	a vector of size 2; the 95% confidence interval for \hat{Δ}_S(t,t_0) based on sample quantiles of the perturbed values; if conf.int = TRUE.
conf.int.normal.R.s	a vector of size 2; the 95% confidence interval for \hat{R}_S(t,t_0) based on a normal approximation; if conf.int = TRUE.
conf.int.quantile.R.s	a vector of size 2; the 95% confidence interval for \hat{R}_S(t,t_0) based on sample quantiles of the perturbed values; if conf.int = TRUE.
conf.int.fieller.R.s	a vector of size 2; the 95% confidence interval for \hat{R}_S(t,t_0) based on Fieller's approach; if conf.int = TRUE.
delta.t	the estimate, \hat{Δ}_T(t,t_0); if incremental.vaue = TRUE.
R.t	the estimated proportion of treatment effect explained by survival only, \hat{R}_T(t,t_0); if incremental.vaue = TRUE.
incremental.value	the estimate of the incremental value of the surrogate markers, \hat{IV}_S(t,t_0); if incremental.vaue = TRUE.
delta.t.var	the variance estimate of \hat{Δ}_T(t,t_0); if var = TRUE or conf.int = TRUE and incremental.vaue = TRUE.
R.t.var	the variance estimate of \hat{R}_T(t,t_0); if var = TRUE or conf.int = TRUE and incremental.vaue = TRUE.
incremental.value.var	the variance estimate of \hat{IV}_S(t,t_0); if var = TRUE or conf.int = TRUE and incremental.vaue = TRUE.
conf.int.normal.delta.t	a vector of size 2; the 95% confidence interval for \hat{Δ}_T(t,t_0) based on a normal approximation; if conf.int = TRUE and incremental.vaue = TRUE.
conf.int.quantile.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 and incremental.vaue = TRUE.
conf.int.normal.R.t	a vector of size 2; the 95% confidence interval for \hat{R}_T(t,t_0) based on a normal approximation; if conf.int = TRUE and incremental.vaue = TRUE.
conf.int.quantile.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 and incremental.vaue = TRUE.
conf.int.fieller.R.t	a vector of size 2; the 95% confidence interval for \hat{R}_T(t,t_0) based on Fieller's approach; if conf.int = TRUE and incremental.vaue = TRUE.
conf.int.normal.iv	a vector of size 2; the 95% confidence interval for \hat{IV}_S(t,t_0) based on a normal approximation; if conf.int = TRUE and incremental.vaue = TRUE.
conf.int.quantile.iv	a vector of size 2; the 95% confidence interval for \hat{IV}_S(t,t_0) based on sample quantiles of the perturbed values; if conf.int = TRUE and incremental.vaue = TRUE.

Note

If the treatment effect is not significant, the user will receive the following message: "Warning: it looks like the treatment effect is not significant; may be difficult to interpret the residual treatment effect in this setting". If the treatment effect is negative, the user will receive the following message: "Warning: it looks like you need to switch the treatment groups" as this package assumes throughout that larger values of the event time are better.

Author(s)

Layla Parast

References

Parast, L., Cai, T., & Tian, L. (2017). Evaluating surrogate marker information using censored data. Statistics in Medicine, 36(11), 1767-1782.

Details can be found in Parast, L., Cai, T., Tian, L. (2020+). Evaluating Multiple Surrogate Markers with Censored Data. Under Review.

Examples

data(d_example_multiple)
names(d_example_multiple)
## Not run: 
R.multiple.surv(xone = d_example_multiple$x1, xzero = d_example_multiple$x0, deltaone = 
d_example_multiple$delta1, deltazero = d_example_multiple$delta0, sone = 
as.matrix(d_example_multiple$s1), szero = as.matrix(d_example_multiple$s0), 
type =1, t = 1, landmark=0.5)
R.multiple.surv(xone = d_example_multiple$x1, xzero = d_example_multiple$x0, deltaone = 
d_example_multiple$delta1, deltazero = d_example_multiple$delta0, sone = 
as.matrix(d_example_multiple$s1), szero = as.matrix(d_example_multiple$s0), 
type =1, t = 1, landmark=0.5, conf.int = T)	
R.multiple.surv(xone = d_example_multiple$x1, xzero = d_example_multiple$x0, deltaone = 
d_example_multiple$delta1, deltazero = d_example_multiple$delta0, sone = 
as.matrix(d_example_multiple$s1), szero = as.matrix(d_example_multiple$s0), 
type =3, t = 1, landmark=0.5)

## End(Not run)

laylaparast/Rsurrogate documentation built on Sept. 28, 2022, 12:25 p.m.