| Title: | Valid Improved Sparsity A-Learning for Optimal Treatment Decision |
|---|---|
| Description: | Valid Improved Sparsity A-Learning (VISA) provides a new method for selecting important variables involved in optimal treatment regime from a multiply robust perspective. The VISA estimator achieves its success by borrowing the strengths of both model averaging (ARM, Yuhong Yang, 2001) <doi:10.1198/016214501753168262> and variable selection (PAL, Chengchun Shi, Ailin Fan, Rui Song and Wenbin Lu, 2018) <doi:10.1214/17-AOS1570>. The package is an implementation of Zishu Zhan and Jingxiao Zhang. (2022+). |
| Authors: | Zishu Zhan [aut, cre], Jingxiao Zhang [aut] |
| Maintainer: | Zishu Zhan <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2025-03-04 04:02:09 UTC |
| Source: | https://github.com/cran/visaOTR |
An Example of Simulated Data for visa
visa_SimuDatavisa_SimuData
The dataset visa_SimuData contains n = 50 samples with p = 10 covariates and treatment variable
the response
the covariates
the treatment received
Valid Improved Sparsity A-Learning for Optimal Treatment Decision
visa.est( y, x, a, IC = c("BIC", "CIC", "VIC"), kap = NULL, lambda.list = exp(seq(-3.5, 2, 0.1)), refit = TRUE )visa.est( y, x, a, IC = c("BIC", "CIC", "VIC"), kap = NULL, lambda.list = exp(seq(-3.5, 2, 0.1)), refit = TRUE )
y |
Vector of response (the larger the better) |
x |
Matrix of model covariates. |
a |
Vector of treatment received. It is a 0/1 index vector representing the subject is in control/treatment group. For details see Example section. |
IC |
Information criterion used in determining the regularization parameter. Users can choose among |
kap |
The model complexity penalty used in the information criteria. By default, kappa = 1 if BIC or CIC is used and kap = 4 if VIC is used. |
lambda.list |
A list of regularization parameter values. Default is exp(seq(-3.5, 2, 0.1)) |
refit |
logical. If |
See the paper provided in Reference section.
an object of class "visa" is a list containing at least the following components:
beta.est |
A vector of coefficients of optimal treatment regime. |
pi.est |
A vector of estimated propensity score. |
h.est |
A vector of estimated baseline function. |
Shi, C., Fan, A., Song, R. and Lu, W. (2018) High-Dimensional A-Learing for Optimal Dynamic Treatment Regimes. Annals of Statistics, 46: 925-957. DOI:10.1214/17-AOS1570
Shi, C.,Song, R. and Lu, W. (2018) Concordance and Value Information Criteria for Optimal Treatment Decision. Annals of Statistics, 49: 49-75. DOI:10.1214/19-AOS1908
Zhan, Z. and Zhang, J. (2022+) Valid Improved Sparsity A-learning for Optimal Treatment Decision. Under review.
data(visa_SimuData) y = visa_SimuData$y a = visa_SimuData$a x = visa_SimuData$x # estimation result <- visa.est(y, x, a, IC = "BIC", lambda.list = c(0.1, 0.5)) result$beta.est result$pi.est result$h.estdata(visa_SimuData) y = visa_SimuData$y a = visa_SimuData$a x = visa_SimuData$x # estimation result <- visa.est(y, x, a, IC = "BIC", lambda.list = c(0.1, 0.5)) result$beta.est result$pi.est result$h.est