sim_Kstage {DTRlearn2} | R Documentation |
Simulate a K-stage Sequential Multiple Assignment Randomized Trial (SMART) data
Description
This function simulates a K-stage SMART data with (pinfo + pnoise)
baseline variables from a multivariate Gaussian distribution. The pinfo
variables have variance 1 and pairwise correlation 0.2; the pnoise
variables have mean 0 and are uncorrelated with each other and with the pinfo
variables.
Subjects are from n_cluster
latent groups with equal sizes, and these n_cluster
groups are characterized by their differentiable means in the pinfo
feature variables. Each latent group has its own optimal treatment sequence, where the optimal treatment for subjects in group g at stage k is generated as A^* = 2( [ g/(2k -1) ]
mod 2) - 1. The assigned treatment group (1 or -1) for each subject at each stage is randomly generated with equal probability. The primary outcome is observed only at the end of the trial, which is generated as
R = \sum_{k=1}^{K} A_k A_k^* + N(0,1)
.
Usage
sim_Kstage (n, n_cluster, pinfo, pnoise, centroids=NULL, K)
Arguments
n |
sample size, should be a multiple of |
n_cluster |
number of latent groups |
pinfo |
number of informative baseline variables |
pnoise |
number of non-informative baseline variables |
centroids |
centroids of the |
K |
number of stages. |
Value
X |
baseline variables. It is a matrix of dimension |
A |
treatment assigments for the K-stages. It is a list of K vectors. |
R |
outcomes of the K-stages. It is a list of K vectors. In this simulation setting, no intermediate outcomes are observed, so the first K-1 vectors are vectors of 0. |
optA |
optimal treatments for the K-stages. It is a list of K vectors. |
centroids |
centroids of the |
Author(s)
Yuan Chen, Ying Liu, Donglin Zeng, Yuanjia Wang
Maintainer: Yuan Chen <yc3281@columbia.edu><irene.yuan.chen@gmail.com>
See Also
Examples
n_train = 100
n_test = 500
n_cluster = 10
pinfo = 10
pnoise = 20
# simulate a 2-stage training set
train = sim_Kstage(n_train, n_cluster, pinfo, pnoise, K=2)
# simulate an independent 2-stage test set with the same centroids of the training set
test = sim_Kstage(n_test, n_cluster, pinfo, pnoise, train$centroids, K=2)