hetsurrSurv (version 1.0)

R.main.estimate: 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)

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

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

Author

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
	# \donttest{
	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)
	# }