delta.e.estimate {hettest}

Tests for a treatment effect on the primary outcome using surrogate marker information, ignoring potential heterogeneity

Description

Nonparametric test for a treatment effect on the primary outcome using surrogate marker information, ignoring potential heterogeneity. This test borrows information from a prior study about the relationship between the surrogate and the primary outcome to test for a treatment effect in the current study.

Usage

delta.e.estimate(sone = NULL, szero = NULL, szerop, yzerop, extrapolate = TRUE, 
mat = NULL, n1 = NULL, n0 = NULL)

Arguments

sone

surrogate marker in the treated group in the current study

szero

surrogate marker in the control group in the current study

szerop

surrogate marker in the control group in the prior study

yzerop

primary outcome in the control group in the prior study

extrapolate

TRUE or FALSE; extrapolate for values outside of the support in the prior study

mat

for the current study, the user can either provide sone and szero or can provide a vector, mat, where the first n1 values are the surrogate marker in the treated group in the current study, and the remaining values are the surrogate marker in the control group in the current study

n1

sample size of treated group in the current study; only needed if mat is provided instead of sone and szero

n0

sample size of control group in the current study; only needed if mat is provided instead of sone and szero

Value

delta.e

estimated treatment effect using surrogate marker information

sd.e

estimated standard error of treatment effect estimate

test.statistic.e

test statistic for treatment effect

p.value.e

p-value for test statistic

Author(s)

Layla Parast

References

Parast, Cai, and Tian (2022+). Using a Surrogate with Heterogeneous Utility to Test for a Treatment Effect.

Examples

data(example.data)
delta.e.estimate(sone = example.data$s1, szero = example.data$s0, szerop = example.data$s0.p, 
yzerop = example.data$y0.p)