Estimation and Validation Methods for Subgroup Identification and Personalized Medicine

Provides functions for fitting and validation of models for subgroup identification and personalized medicine / precision medicine under the general subgroup identification framework of Chen et al. (2017) . This package is intended for use for both randomized controlled trials and observational studies.


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The ‘personalized’ package is designed for the analysis of data where the effect of a treatment or intervention may vary for different patients. It can be used for either data from randomized controlled trials or observational studies and is not limited specifically to the analysis of medical data.

The personalized package provides estimation methods for subgroup identification under the framework of Chen et al (2017). It also provides routines for valid estimation of the subgroup-specific treatment effects.

Documentation

Documentation

Installing the ‘personalized’ package

Install from CRAN using:

install.packages("personalized")

or install the development version using the devtools package:

devtools::install_github("jaredhuling/personalized")

or by cloning and building using R CMD INSTALL

Quick Usage Overview

Load the package:

library(personalized)

Create a propensity score model

(it should be a function which inputs covariates and treatments and returns propensity score):

prop.func <- function(x, trt)
{
    # fit propensity score model
    propens.model <- cv.glmnet(y = trt,
                               x = x, family = "binomial")
    pi.x <- predict(propens.model, s = "lambda.min",
                    newx = x, type = "response")[,1]
    pi.x
}

Fit a model to estimate subgroup:

subgrp.model <- fit.subgroup(x = x, y = y,
                             trt = trt,
                             propensity.func = prop.func,
                             loss   = "sq_loss_lasso",
                             nfolds = 5)              # option for cv.glmnet

Display estimated subgroups and variables selected which determine the subgroups:

summary(subgrp.model)
## family:    gaussian 
## loss:      sq_loss_lasso 
## method:    weighting 
## cutpoint:  0 
## propensity 
## function:  propensity.func 
## 
## benefit score: f(x), 
## Trt recomm = Trt*I(f(x)>c)+Ctrl*I(f(x)<=c) where c is 'cutpoint'
## 
## Average Outcomes:
##                 Recommended Ctrl    Recommended Trt
## Received Ctrl  -4.2429 (n = 117) -21.9576 (n = 114)
## Received Trt  -23.6902 (n = 132)  -6.7605 (n = 137)
## 
## Treatment effects conditional on subgroups:
## Est of E[Y|T=Ctrl,T=Recom]-E[Y|T=/=Ctrl,T=Recom] 
##                                19.4474 (n = 249) 
##   Est of E[Y|T=Trt,T=Recom]-E[Y|T=/=Trt,T=Recom] 
##                                15.1972 (n = 251) 
## 
## NOTE: The above average outcomes are biased estimates of
##       the expected outcomes conditional on subgroups. 
##       Use 'validate.subgroup()' to obtain unbiased estimates.
## 
## ---------------------------------------------------
## 
## Benefit score quantiles (f(X) for Trt vs Ctrl): 
##        0%       25%       50%       75%      100% 
## -14.15602  -3.58120   0.04648   3.51676  14.78106 
## 
## ---------------------------------------------------
## 
## Summary of individual treatment effects: 
## E[Y|T=Trt, X] - E[Y|T=Ctrl, X]
## 
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -28.31203  -7.16240   0.09296   0.27303   7.03352  29.56212 
## 
## ---------------------------------------------------
## 
## 9 out of 50 interactions selected in total by the lasso (cross validation criterion).
## 
## The first estimate is the treatment main effect, which is always selected. 
## Any other variables selected represent treatment-covariate interactions.
## 
##             Trt    V2     V11     V17    V32   V35     V39    V40    V45
## Estimate 0.3389 1.312 -0.8576 -0.3681 0.2421 0.357 -0.1401 0.0275 0.0945
##              V50
## Estimate -0.0422

Use repeated train and test splitting to estimate subgroup treatment effects:

val.model <- validate.subgroup(subgrp.model, B = 100,
                               method = "training_test",
                               train.fraction = 0.75)

Display estimated subgroup treatment effects:

print(val.model, digits = 2, sample.pct = TRUE)
## family:  gaussian 
## loss:    sq_loss_lasso 
## method:  weighting 
## 
## validation method:  training_test_replication 
## cutpoint:           0 
## replications:       100 
## 
## benefit score: f(x), 
## Trt recomm = Trt*I(f(x)>c)+Ctrl*I(f(x)<=c) where c is 'cutpoint'
## 
## Average Test Set Outcomes:
##                         Recommended Ctrl            Recommended Trt
## Received Ctrl -10.85 (SE = 7.88, 20.74%)  -18.64 (SE = 6.5, 25.81%)
## Received Trt   -15.81 (SE = 5.9, 24.18%) -15.36 (SE = 9.02, 29.26%)
## 
## Treatment effects conditional on subgroups:
## Est of E[Y|T=Ctrl,T=Recom]-E[Y|T=/=Ctrl,T=Recom] 
##                        4.97 (SE = 11.23, 44.93%) 
##   Est of E[Y|T=Trt,T=Recom]-E[Y|T=/=Trt,T=Recom] 
##                        3.27 (SE = 11.73, 55.07%) 
## 
## Est of 
## E[Y|Trt received = Trt recom] - E[Y|Trt received =/= Trt recom]:                
## 1.2 (SE = 8.77)

Visualize subgroup-specific treatment effect estimates across training/testing iterations:

plot(val.model)

Investigate the marginal characteristics of the two estimated subgroups

Here we only display covariates with a significantly different mean value (at level 0.05)

summ <- summarize.subgroups(subgrp.model)
print(summ, p.value = 0.05)
##     Avg (recom Ctrl) Avg (recom Trt) Ctrl - Trt pval Ctrl - Trt
## V2           -1.8994          1.8703    -3.7697       6.746e-55
## V11           1.2394         -1.1216     2.3610       1.325e-21
## V17           0.9566         -0.6530     1.6096       3.814e-09
## V32          -0.4991          0.1747    -0.6738       9.574e-03
## V35          -0.7170          0.4642    -1.1812       1.356e-05
## V39           0.3090         -0.3552     0.6643       1.700e-02
## V43          -0.2931          0.3787    -0.6718       1.706e-02
##     SE (recom Ctrl) SE (recom Trt)
## V2           0.1469         0.1536
## V11          0.1753         0.1577
## V17          0.1989         0.1800
## V32          0.1715         0.1941
## V35          0.1825         0.1973
## V39          0.1906         0.2016
## V43          0.2091         0.1872

Accessing Help Files for Main Functions of personalized

Access help files for the main functions of the personalized package:

?fit.subgroup
?validate.subgroup

News

Reference manual

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install.packages("personalized")

0.2.4 by Jared Huling, 2 months ago


https://jaredhuling.github.io/personalized/, https://arxiv.org/abs/1809.07905


Report a bug at https://github.com/jaredhuling/personalized/issues


Browse source code at https://github.com/cran/personalized


Authors: Jared Huling [aut, cre] , Aaron Potvien [ctb] , Alexandros Karatzoglou [cph] , Alex Smola [cph]


Documentation:   PDF Manual  


GPL-2 license


Imports survival, methods, kernlab, foreach

Depends on glmnet, mgcv, gbm, ggplot2, plotly

Suggests knitr, rmarkdown, testthat


See at CRAN