R.main.estimate {hetsurrSurv} | R Documentation |
Estimates the proportion of treatment effect explained by the surrogate marker as a function of a baseline covariate
Description
Assesses heterogeneity in the utility of a surrogate marker with respect to a baseline covariate using censored (survival data) by estimates the proportion of treatment effect explained by the surrogate marker as a function of a baseline covariate, w
Usage
R.main.estimate(xone, xzero, deltaone, deltazero, sone, szero, wone, wzero, w.grd, myt,
landmark, type = "cont", var = FALSE, test = FALSE, extrapolate = TRUE, h.0 = NULL,
h.1 = NULL, h.w = NULL, h.s = NULL, h.w.1 = NULL)
Arguments
xone |
x1, observed event time in the treated group |
xzero |
x0, observed event time in the control group |
deltaone |
delta1, event indicator in the treated group |
deltazero |
delta0, event indicator in the control group |
sone |
s1, surrogate marker in the treated group |
szero |
s0, surrogate marker in the control group |
wone |
w1, baseline covariate in the treated group |
wzero |
w0, baseline covariate in the control group |
w.grd |
grid for w where estimation will be provided |
myt |
t of interest |
landmark |
t0, landmark time |
type |
options are "cont" or "discrete"; type of baseline covariate, default is "cont" |
var |
TRUE or FALSE, if variance/standard error estimates are wanted |
test |
TRUE or FALSE, if test for heterogeneity is wanted wanted |
extrapolate |
TRUE or FALSE |
h.0 |
bandwidth |
h.1 |
bandwidth |
h.w |
bandwidth |
h.s |
bandwidth |
h.w.1 |
bandwidth |
Value
A list is returned:
w.values |
grid for w where estimation is provided |
R.s.w |
The propoportion of treatment effect explained as a function of the baseline covariate, w |
delta.w |
The treatment effect as a function of the baseline covariate, w |
delta.s.w |
The residual treatment effect as a function of the baseline covariate, w |
sd.R |
Standard error estimate of R.s.w |
sd.delta |
Standard error estimate of delta.w |
sd.delta.s |
Standard error estimate of delta.s.w |
pval.omnibus |
p-value from the omnibus test for heterogeneity |
pval.con |
p-value from the conservative omnibus test for heterogeneity |
Author(s)
Layla Parast
References
Parast L, Tian L, Cai, T. (2024) "Assessing Heterogeneity in Surrogacy Using Censored Data." Statistics in Medicine, 43(17): 3184-3209.
Examples
data(example.data)
names(example.data)
R.main.estimate(xone=example.data$x1, xzero=example.data$x0, deltaone=example.data$d1,
deltazero=example.data$d0, sone=log(example.data$s1), szero=log(example.data$s0),
wone=log(example.data$w1), wzero=log(example.data$w0),
w.grd=log(seq(0.1,0.9, length=25)), myt=1, landmark=0.5)
R.main.estimate(xone=example.data$x1, xzero=example.data$x0, deltaone=example.data$d1,
deltazero=example.data$d0, sone=log(example.data$s1), szero=log(example.data$s0),
wone=example.data$w1_cat, wzero=example.data$w0_cat,
myt=1, landmark=0.5,type = "discrete", w.grd = c(1,2,3,4))
#computationally intensive
R.main.estimate(xone=example.data$x1, xzero=example.data$x0, deltaone=example.data$d1,
deltazero=example.data$d0, sone=log(example.data$s1), szero=log(example.data$s0),
wone=log(example.data$w1), wzero=log(example.data$w0),
w.grd=log(seq(0.1,0.9, length=25)), myt=1, landmark=0.5, test=TRUE)