| Title: | Assessing Surrogacy with a Censored Outcome |
|---|---|
| Description: | Identifies the optimal transformation of a surrogate marker and estimates the proportion of treatment explained (PTE) by the optimally-transformed surrogate at an earlier time point when the primary outcome of interest is a censored time-to-event outcome; details are described in Wang et al (2021) <doi:10.1002/sim.9185>. |
| Authors: | Xuan Wang [aut], Clara-Lea Bonzel [aut], Tianxi Cai [aut], Layla Parast [aut, cre], PARSE LTD [aut] |
| Maintainer: | Layla Parast <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0 |
| Built: | 2024-11-11 03:28:17 UTC |
| Source: | https://github.com/cran/OSsurvival |
Simulated data for the example.
data.exampledata.example
A data list with 5 elements:
time at which the surrogate is measured
time at which the primary outcome is measured
observed survival time
surrogate information at t.0
event indicator
treatment indicator
Estimates the proportion of treatment effect explained by the optimally transformed surrogate
pte.survival(xob, s.ob, deltaob, aob, t, t.0, varind = 0, re = 100)pte.survival(xob, s.ob, deltaob, aob, t, t.0, varind = 0, re = 100)
xob |
observed survival time |
s.ob |
surrogate information at time t.0 |
deltaob |
event indicator |
aob |
treatment indicator |
t |
time at which the primary outcome is measured |
t.0 |
time at which the surrogate is measured |
varind |
whether to estimate variance (yes=0, no=1) |
re |
number of replications for resampling, if varind=0 |
A list of the following:
pte.est |
The estimated proportion of treatment effect explained (PTE) by the optimally transformed surrogate |
pte.ese |
Standard error estimate for the PTE, provided if var.ind=0 |
g1.est |
Estimated g1 |
g1.ese |
Standard error estimate for ge, provided if var.ind = 0 |
sgrid |
Grid used for the surrogate marker, equally spaced |
gs.est |
Estimated g(s), optimal transformation of s, for the sgrid |
gs.ese |
Standard error estimate for g(s), provided if var.ind = 0 |
# load the data data("sysdata") # time at which the surrogate is measured t.0 = data.example$t.0 # time at which the primary outcome is measured t = data.example$t # observed survival time xob = data.example$data$xob # surrogate information at t.0 s.ob = data.example$data$s.ob # event indicator deltaob = data.example$data$deltaob # treatment indicator aob = data.example$data$aob # main estimation function # varind: whether to estimate variance; re:number of replications for resampling out = pte.survival(xob, s.ob, deltaob, aob, t, t.0, varind=0, re=100) # estimated PTE out$pte.est # estimated g1 out$g1.est # estimated g2(s) at equally spaced s point plot(out$sgrid, out$gs.est, type="l", xlab = "Surrogate Marker", ylab = "Optimal Transformation") #The PTE result indicates that this is a moderate to high surrogate marker in this setting.# load the data data("sysdata") # time at which the surrogate is measured t.0 = data.example$t.0 # time at which the primary outcome is measured t = data.example$t # observed survival time xob = data.example$data$xob # surrogate information at t.0 s.ob = data.example$data$s.ob # event indicator deltaob = data.example$data$deltaob # treatment indicator aob = data.example$data$aob # main estimation function # varind: whether to estimate variance; re:number of replications for resampling out = pte.survival(xob, s.ob, deltaob, aob, t, t.0, varind=0, re=100) # estimated PTE out$pte.est # estimated g1 out$g1.est # estimated g2(s) at equally spaced s point plot(out$sgrid, out$gs.est, type="l", xlab = "Surrogate Marker", ylab = "Optimal Transformation") #The PTE result indicates that this is a moderate to high surrogate marker in this setting.