param_ple: Parameter Estimation: Patient-Level Estimates

Description Usage Arguments Value References Examples

View source: R/param_ple.R

Description

For each identified subgroup and in the overall population, average the patient-level estimates of E(Y|A=1), E(Y|A=0), and E(Y|A=1)-E(Y|A=0). Pseudo-outcomes are used for variance estimates (Jemielita and Mehrotra 2019).

Usage

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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.

References

Jemielita T, Mehrotra D. PRISM: Patient Response Identifiers for Stratified Medicine. https://arxiv.org/abs/1912.03337

Examples

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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

StratifiedMedicine documentation built on March 1, 2020, 9:07 a.m.