StratifiedMedicine (version 0.1.2)

param_rmst: Parameter Estimation: Restricted Mean Survival Time (RMST)

Description

For each identified subgroup, estimate the restricted mean survival time (RMST), based on the method described in the R package "survRM2". Point-estimates and variability metrics in the overall population are obtained by aggregating subgroup specific results (adaptive weighting or sample size weighting).

Usage

param_rmst(Y, A, X, mu_hat, Subgrps, alpha_ovrl, alpha_s,
  combine = "adaptive", ...)

Arguments

Y

The outcome variable. Must be numeric or survival (ex; Surv(time,cens) )

A

Treatment variable. (a=1,...A)

X

Covariate space.

mu_hat

Patient-level estimates (See PLE_models)

Subgrps

Identified subgroups (can be the overall population)

alpha_ovrl

Two-sided alpha level for overall population

alpha_s

Two-sided alpha level at subgroup

combine

For overall population, method of combining subgroup-specific results. Default is "adaptive", "SS" corresponds to sample size weighting.

...

Any additional parameters, not currently passed through.

Value

Data-set with parameter estimates (RMST) and corresponding variability metrics, for overall and subgroups.

• param.dat - Parameter estimates and variability metrics

See Also

param_combine

Examples

# NOT RUN {
# }
# NOT RUN {
library(StratifiedMedicine)
# Survival Data #
require(TH.data); require(coin)
data("GBSG2", package = "TH.data")
surv.dat = GBSG2
# Design Matrices ###
Y = with(surv.dat, Surv(time, cens))
X = surv.dat[,!(colnames(surv.dat) %in% c("time", "cens")) ]
A = rbinom( n = dim(X)[1], size=1, prob=0.5  ) ## simulate null treatment

# MOB-Weibull Subgroup Model ##
res_weibull = submod_train(Y, A, X, Xtest=X, family="survival",
                           submod = "submod_weibull")
plot(res_weibull$mod)

# Parameter-Estimation ##
require(survRM2)
params = param_rmst(Y, A, X, Subgrps = res_weibull$Subgrps.train, alpha_ovrl=0.05,
                   alpha_s=0.05)
params
# }
# NOT RUN {
# }