delta.multiple.surv {Rsurrogate} | R Documentation |
Calculates robust residual treatment effect accounting for multiple surrogate markers at a specified time and primary outcome information up to that specified time
Description
This function calculates the robust estimate of the residual treatment effect accounting for multiple surrogate markers measured at and primary outcome information up to
i.e. the hypothetical treatment effect if both the surrogate marker distributions at
and survival up to
in the treatment group look like the surrogate marker distributions and survival up to
in the control group. Ideally this function is only used as a helper function and is not directly called.
Usage
delta.multiple.surv(xone, xzero, deltaone, deltazero, sone, szero, type =1, t,
weight.perturb = NULL, landmark, extrapolate = FALSE, transform = 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 |
szero |
matrix of numeric values; surrogate marker measurements at |
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 |
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 psuedo-score. |
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 are included in the documentation for R.multiple.surv.
Value
, the residual treatment effect estimate accounting for multiple surrogarte markers measured at
and primary outcome information up to
.
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".
Author(s)
Layla Parast
References
Parast, L., Cai, T., & Tian, L. (2021). Evaluating multiple surrogate markers with censored data. Biometrics, 77(4), 1315-1327.
Examples
data(d_example_multiple)
names(d_example_multiple)
## Not run:
delta.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)
## End(Not run)