longsurr (version 1.1)

sjm_nl_estimate: Semiparametric Joint Modeling of the Treatment Effect on a Longitudinal Surrogate with a Nonlinear Model

Description

Semiparametric joint modeling of the treatment effect on a longitudinal surrogate using both a Cox proportional hazards model and a splines-based model

Usage

sjm_nl_estimate(X, Time, Delta, obsT, Y, gap_time = 0.1, n.resample = 100, 
var = FALSE)

Value

A list of estimates is returned:

est

estimated hazard ratio from the Cox model

est_t

vector of estimated treatment effect on the slope of the surrogate marker i.e., the surrogate marker trajectory, on a grid constructed from the given gap time

t_grid

vector of grid times corresponding to the returned estimates

SE_est

if var is TRUE, standard error estimate of the hazard ratio

SE_est_t

if var is TRUE, standard error estimate of the estimated treatment effect on the slope of the surrogate marker

CI_lower_est

if var is TRUE, lower bound of the 95% confidence interval for the hazard ratio

CI_upper_est

if var is TRUE, upper bound of the 95% confidence interval for the hazard ratio

CI_lower_est_t

if var is TRUE, lower bound of the 95% confidence interval for the treatment effect on the slope of the surrogate marker

CI_upper_est_t

if var is TRUE, upper bound of the 95% confidence interval for the treatment effect on the slope of the surrogate marker

Arguments

X

numeric vector containing the treatment indicator for each observation with 1 for treated and 0 for control

Time

numeric vector containing the observed event or censoring time for each observation

Delta

numeric vector containing the event indicator for each observation

obsT

numeric matrix containing the time that the surrogate marker was measured for each observation; number of rows should be equal to the number of observations and number of columns should be equal to the maximum number of surrogate markers measured. If the surrogate marker was not measured, the corresponding entry should be 0 or NA.

Y

numeric matrix containing the the surrogate marker measurements over time for each observation; number of rows should be equal to the number of observations and number of columns should be equal to the maximum number of surrogate markers measured. If the surrogate marker was not measured, as determined by the obsT entry, the Y at that time will be ignored.

gap_time

number indicating gap time for slope estimation; default is 0.1.

n.resample

number of resampled estimates used for variance estimation; default is 100.

var

logical indicating whether the user would like variance estimates and confidence intervals; default is FALSE.

Author

Xuan Wang

References

Wang X, Zhou J, Parast L, Greene T (2025). Semiparametric Joint Modeling to Estimate the Treatment Effect on a Longitudinal Surrogate with Application to Chronic Kidney Disease Trials. Biometrics, 81(3): ujaf104.

Examples

data(data_sjm)
# \donttest{

sjm_nl_estimate(X=data_sjm$Treatment, Time = data_sjm$Time, 
Delta = data_sjm$delta, obsT = data_sjm$obsT, Y = data_sjm$Y, gap_time=0.2)

sjm_nl_estimate(X=data_sjm$Treatment, Time = 
data_sjm$Time, Delta = data_sjm$delta, obsT = data_sjm$obsT, 
Y = data_sjm$Y, gap_time = 0.2, n.resample=5, var=TRUE)# }