StratifiedMedicine (version 0.1.3)

param_ple: Parameter Estimation: Patient-Level Estimates

Description

Parameter estimation and inference through patient-level estimates. Usable for continuous and binary outcomes (possibly survival, needs further evaluation).

Usage

param_ple(Y, A, X, mu_hat, Subgrps, alpha_ovrl, alpha_s, ...)

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

...

Any additional parameters, not currently passed through.

Value

Data-set with parameter estimates and corresponding variability metrics, for overall and subgroups. Subgrps=0 corresponds to the overall population by default.

• param.dat - Parameter estimates and variability metrics (est, SE, LCL/UCL = lower/upper confidence limits, pval = p-value).

Examples

# NOT RUN {
library(StratifiedMedicine)

## Continuous ##
dat_ctns = generate_subgrp_data(family="gaussian")
Y = dat_ctns$Y
X = dat_ctns$X
A = dat_ctns$A
train = data.frame(Y, A, X)

## Estimate PLEs (ranger) ##
res_ranger = ple_train(Y, A, X, Xtest=X, ple = "ple_ranger")

## Identify Subgroups: MOB (lmtree) ##
res_lmtree = submod_train(Y, A, X, Xtest=X, submod="submod_lmtree")

## Parameter-estimation ##
params = param_ple(Y, A, X, mu_hat = res_ranger$mu_train,
                  Subgrps = res_lmtree$Subgrps.train, alpha_ovrl=0.05,
                  alpha_s=0.05)
params


# }