Citation
Wang T, Keil AP, Kim S, Wyss R, Htoo PT, Funk MJ, Buse JB, Kosorok MR, Stürmer T. Iterative Causal Forest: A Novel Algorithm for Subgroup Identification. Am J Epidemiol. 2024 May 7;193(5):764-776 https://doi.org/10.1093/aje/kwad219.
The iCF algorithm (based on causal forest) identifies important subgroups with heterogeneous treatment effects without prior knowledge of treatment-covariate interactions
Iterative causal forest (iCF) algorithm. CF, causal forest; RF, random forest;
1. R packages recommended
# List of packages to install
packages_to_install <- c("MASS", "grf", "tidyverse", "rlang", "rlist", "plyr", "caret", "caTools", "listdtr", "randomForest", "ggplot2",
"ggridges", "data.table", "grid", "cobalt", "broom", "optmatch", "rstatix", "ggpubr", "DMwR", "knitr", "DiagrammeR")
#install.packages(packages_to_install)
# Load the packages into the R session
lapply(packages_to_install, library, character.only = TRUE)
# Additional command to set a specific theme in ggplot2
theme_set(theme_ridges())
2. Installation
There are three ways to install iCF.
First, download all the R programs in the R folder and save them in your local directory (create a new folder named 'iCF'). Then, run the following R codes each time before executing iCF:
source("/local/iCF/best_tree_MSegar.R")
source("/local/iCF/iCF_CV.R")
source("/local/iCF/iCF_TREE_build.R")
source("/local/iCF/iCF_PARENT_node.R")
source("/local/iCF/iCF_PRE_majority.R")
source("/local/iCF/iCF_MAJORITY_VOTE.R")
source("/local/iCF/iCF_SUBGROUP_DECISION.R")
source("/local/iCF/iCF_SUBGROUP_PIPELINE.R")
source("/local/iCF/iCF_SUBGROUP_ANALYSIS.R")
source("/local/iCF/iCF_SUBGROUP_MODEL.R")
source("/local/iCF/iCF_GG_toolbox.R")
source("/local/iCF/sim_Truth_tree.R")
Second, you can install it using devtools (this option is currently being worked on and will be available soon):
install.packages('devtools')
devtools::install_github("tianshengwang/iCF")
library(iCF)
Third, you can download the 'iCF_0.0.0.9000.tar.gz' file (this option is currently being worked on and will be available soon)..."
install.packages("iCF_0.0.0.9000.tar.gz", repos = NULL, type ="source")
library(iCF)
3. Data Simulation
We used a simulation setup similar to the one initially described by Setoguchi et al. and modified it by incorporating treatment-covariate interactions to model heterogeneous treatment effects.
nstudy = 10000
b0 <- 0
b1 <- 0.8
b2 <- -0.25
b3 <- 0.6
b4 <- -0.4
b5 <- -0.8
b6 <- -0.5
b7 <- 0.7
a0 <- -3.85
a1 <- 0.3
a2 <- -0.36
a3 <- -0.73
a4 <- -0.2
a5 <- 0.71
a6 <- -0.19
a7 <- 0.26
g1 <- -0.4
F.sample.cor <- function(X, rho) {
Y <- (rho * (X - mean(X)))/sqrt(var(X)) + sqrt(1 - rho^2) * rnorm(length(X))
return(Y)
}
#4 standard normal: X2, X4, X7, X10;
X2 <- rnorm(nstudy, mean=0, sd=1)
X4 <- rnorm(nstudy, mean=0, sd=1)
X7 <- rnorm(nstudy, mean=0, sd=1)
X10 <- rnorm(nstudy, mean=0, sd=1)
#6 binary variables: X1, X3, X5, X6, X8, X9;
X1 <- rnorm(nstudy, mean=0, sd=1)
X1 <- ifelse(X1 > mean(X1), 1, 0)
X3 <- rnorm(nstudy, mean=0, sd=1)
X3 <- ifelse(X3 > mean(X3), 1, 0)
X5 <- F.sample.cor(X1, 0.2)
X5 <- ifelse(X5 > mean(X5), 1, 0)
X6 <- F.sample.cor(X2, 0.9)
X6 <- ifelse(X6 > mean(X6), 1, 0)
X8 <- F.sample.cor(X3, 0.2)
X8 <- ifelse(X8 > mean(X8), 1, 0)
X9 <- F.sample.cor(X4, 0.9)
X9 <- ifelse(X9 > mean(X9), 1, 0)
#4 confounders: X1, X2, X3, X4;
#3 outcome predictors: X8, X9, X10;
#1 instrumental X7;
PS <- (1 + exp( -(b0 + b1*X1 + b2*X2 + b3*X3 + b4*X4 + b5*X5 + b6*X6 + b7*X7) ))^-1 #true propensity score
W <- rbinom(nstudy,1,PS)
#3-way interaction of W, X1, and X3 (W:X1:X3)
Y = a0 + a1*X1 + a2*X2 + a3*X3 + a4*X4 +a5*X8 + a6*X9 + a7*X10 + g1*W + rnorm(nstudy,0,1) + 0.4*W*X3 + 0.3*W*X1 + 0.4*W*X1*X3 + 0.2*X1*X3
Make a dataset in the "data.frame" format to be run by iCF: the 1st column is outcome Y, 2nd column is treatment W, and remaining columns features X.
Train <<- as.data.frame(cbind(Y, W, X1, X2, X3 ,X4, X5, X6, X7, X8, X9, X10))
4. Run iCF on simulated data
Step 1. Run raw causal forest to predict outcome (Y.hat), propensity score (W.hat), and select variables
vars_forest = colnames( Train %>% dplyr::select(-c("Y", "W" )) )
X <- Train[,vars_forest]
Y <- as.vector( as.numeric( Train[,"Y"] ) )
W <- as.vector( as.numeric( Train[,"W"] ) )
cf_raw_key.tr <- CF_RAW_key(Train, 1, "non-hd", hdPctTop=0.90)
Y.hat <<- cf_raw_key.tr$Y.hat
W.hat <<- cf_raw_key.tr$W.hat
HTE_P_cf.raw <<- cf_raw_key.tr$HTE_P_cf.raw
varimp_cf <- cf_raw_key.tr$varimp_cf
selected_cf.idx <<- cf_raw_key.tr$selected_cf.idx
X[,selected_cf.idx]%>%colnames()
GG_VI(varimp_cf, "Variable importance", colnames(X))
Step 2: Tune the minimum-leaf-size (MLS) for D2, D3, D4, and D5 to ensure that the majority of the best trees from causal forests grown with these MLS have depths of 2, 3, 4, and 5, respectively.
#Specify the decimal position for continuous variables in the subgroup definition.
split_val_round_posi=0
#Define categorical variables with more than two levels:
vars_catover2 <<- find_level_over2(X)
#Be cautious: a very small denominator can result in an excessively large min.node.size (calculated as sample size divided by the denominator).
#This may prevent iCF from splitting and subsequently trigger an error. For illustration, consider using a denominator of 10：
D2_MLS=MinLeafSizeTune(Train, denominator=10, treeNo = 200, iterationNo=25, split_val_round_posi=0, "D2", "steelblue1")
D2_MLS=MinLeafSizeTune(Train, denominator=25, treeNo = 200, iterationNo=100, split_val_round_posi=0, "D2", "steelblue1")
D2_MLS$depth_mean
D2_MLS$depth_gg
Notably, if you got this message "Error: Can't subset columns that don't exist. x Column parent_sign doesn't exist.", it suggests the denominator used for developing causal forest is too small, leading to a too large MLS for D2 forest so that the tree does not even split (the node does not have a parent node). In this scenario, increasing the denominator will solve the problem.
D3_MLS=MinLeafSizeTune(Train, denominator=45, treeNo = 200, iterationNo=100, split_val_round_posi=0, "D3", "steelblue1")
D3_MLS$depth_mean
D3_MLS$depth_gg
D4_MLS=MinLeafSizeTune(Train,denominator=65, treeNo = 200, iterationNo=100, split_val_round_posi=0, "D4", "steelblue1")
D4_MLS$depth_mean
D4_MLS$depth_gg
D5_MLS=MinLeafSizeTune(Train, denominator=85, treeNo = 200, iterationNo=100, split_val_round_posi=0, "D5", "steelblue1")
D5_MLS$depth_mean
D5_MLS$depth_gg
Note that despite using a smaller MLS, the best trees from causal forests do not grow deeper due to the presence of a strong three-way interaction (W:X1:X3) in the simulated data set. In such scenarios, we can proceed with implementing iCF when tuning MLS does not affect the depth of the best tree.
Step 3. Implement iCF on simulated dataset
leafsize <<- list(D5=D5_MLS$denominator, D4=D4_MLS$denominator, D3=D3_MLS$denominator, D2=D2_MLS$denominator)
iCFCV_B1000_i200_sim <- iCFCV(dat=Train, K=5, treeNo=200, iterationNo=10, min.split.var=4,
split_val_round_posi=0, P_threshold=1, variable_type = "non-HD",
hdPctTop= 0.95, HTE_P_cf.raw = HTE_P_cf.raw)
#view subgroup decision
iCFCV_B1000_i200_sim$selectedSG_ori
#view CATE
iCFCV_B1000_i200_sim$CATE_t2_ori %>% select(SubgroupID, Definition, CATE_iptw) %>% distinct()
5. Run iCF on real-world data
We compared the two-year risk difference of hospitalized heart failure (HHF) of initiating any sodium-glucose cotransporter-2 inhibitors (SGLT2i) versus glucagon-like peptide-1 receptor agonists (GLP1RA) using a 20% random sample of all fee-for-service U.S. Medicare beneficiaries who had parts A (inpatient), B (outpatient physician services), and D (dispensed prescription drugs) coverage for at least one month from January 2012 to December 2017. The details of the cohort were described previously by Htoo et al. and are available in the mehtod paper (Wang et al.)
Note the outcome Y should be on risk difference scale. For example, only 2 (Patient 1 & 4) out of the following 5 patients had heart failure hospitalization (black dot) events in a 2-year period.
Make a dataset in the "data.frame" format to be run by iCF: the 1st column is treatment W, 2nd column is outcome Y, and remaining columns features X. And the 1st and 2nd columns should be named following this pattern, i.e., 'W' for treatment and 'Y' for outcome.
load("HHF_SGLTvGLP_iCFCV.RData")
Train <- hfp_2yr_all_sgltvglp %>%
dplyr::filter(FillDate2 <= 21184 -365*2 & #31DEC2017
IndexDate >= 19449 #1APR2013
) %>%
dplyr::mutate(Y = hfp_2yr_2yr,
age = as.numeric(cut(age, c(65,70,75,80,85,Inf) ,
labels=c("65<age<=70 ","70<age<=75","75<age<=80","80<age<=85", "age>85")))) %>%
dplyr::rename(W=SGLT) %>%
dplyr::select(Y, W, age, race2, sex,baselinecvd, baselinechf,
dplyr::starts_with("bl_")) %>%
as.data.frame.matrix() %>%
mutate(sex=as.numeric(sex)) %>%
dplyr::select_if(~ !any(is.na(.))) %>%
as.data.frame()
# Note missing data will result in errors when running iCF/hdiCF like this:
#-------------------------------------------------------
# Error: Problem with `mutate()` column `G4`.
# ℹ `G4 = Dat_ID_SG_D4$SubgroupID`.
# ℹ `G4` must be size 11886 or 1, not 11888.
#-------------------------------------------------------
# Thus, first identify variables with missing with the following codes:
list_vars_with_missing_data <- function(df) {
vars_with_missing <- sapply(df, function(x) anyNA(x))
names(df)[vars_with_missing]
}
vars_with_missing <- list_vars_with_missing_data(Train)
print(vars_with_missing)
Step 1. Run raw causal forest to predict outcome (Y.hat), propensity score (W.hat), and select variables
vars_forest = colnames( Train %>% dplyr::select(-c("Y", "W")) )
X <<- Train[,vars_forest]
Y <<- as.vector( as.numeric( Train[,"Y"] ) )
W <<- as.vector( as.numeric( Train[,"W"] ) )
cf_raw_key.tr <- CF_RAW_key(Train, min.split.var=4, variable_type="non-hd", hdPctTop=0.95)
Y.hat <<- cf_raw_key.tr$Y.hat
W.hat <<- cf_raw_key.tr$W.hat
HTE_P_cf.raw <<- cf_raw_key.tr$HTE_P_cf.raw
varimp_cf <- cf_raw_key.tr$varimp_cf
selected_cf.idx <<- cf_raw_key.tr$selected_cf.idx
time_rawCF <- cf_raw_key.tr$time_rawCF
X[,selected_cf.idx]
GG_VI(varimp_cf, 'Variable Importance for SGLT2i vs GLP1RA cohort for HFF', colnames(X))
Step 2: Tune the MLS for D2, D3, D4, and D5 to ensure that the majority of the best trees from causal forests grown with these MLS have depths of 2, 3, 4, and 5, respectively.
#Specify the decimal position for continuous variables in the subgroup definition.
split_val_round_posi=0
#Define categorical variables with more than two levels:
vars_catover2 <<- find_level_over2(X)
D2_MLS=MinLeafSizeTune(dat=Train, denominator=25, treeNo = 1000, iterationNo=100, split_val_round_posi=0, "D2", "#62C6F2")
D2_MLS$depth_mean
D2_MLS$depth_gg
D3_MLS=MinLeafSizeTune(dat=Train, denominator=45, treeNo = 1000, iterationNo=100, split_val_round_posi=0, "D3", "#62C6F2")
D3_MLS$depth_mean
D3_MLS$depth_gg
D4_MLS=MinLeafSizeTune(dat=Train, denominator=65, treeNo = 1000, iterationNo=100, split_val_round_posi=0, "D4", "#62C6F2")
D4_MLS$depth_mean
D4_MLS$depth_gg
D5_MLS=MinLeafSizeTune(dat=Train, denominator=85, treeNo = 1000, iterationNo=100, split_val_round_posi=0, "D5", "#62C6F2")
D5_MLS$depth_mean
D5_MLS$depth_gg
Step 3. Implement iCF on Medicare SGLT2i vs GLP1RA new user cohort
leafsize <<- list(D5=D5_MLS$denominator, D4=D4_MLS$denominator, D3=D3_MLS$denominator, D2=D2_MLS$denominator)
iCFCV_B1000_i100_rwd <- iCFCV(dat=Train,K=5, treeNo=1000, iterationNo=100, min.split.var=4,
split_val_round_posi=0, P_threshold=1, variable_type = "non-HD",
hdPctTop= 0.95, HTE_P_cf.raw = HTE_P_cf.raw)
#view subgroup decision
iCFCV_B1000_i100_rwd$selectedSG_ori
Empirical Studies Implementing iCF: Wang T, Keil AP, Buse JB, Keet C, Kim S, Wyss R, Pate V, Jonsson-Funk M, Pratley RE, Kvist K, Kosorok MR, Stürmer T. Glucagon-like Peptide 1 Receptor Agonists and Asthma Exacerbations: Which Patients Benefit Most? Ann Am Thorac Soc. 2024 Jul 16. doi: 10.1513/AnnalsATS.202309-836OC
If you have further questions or comments, please contact Dr. Tiansheng Wang: [email protected]