Introduction to R package `survivalisttoo` (with `mlS3`)

library(survivalisttoo) # this package
library(glmnet)

## Loading required package: Matrix

## Loaded glmnet 4.1-10

library(survival)

data(pbc)
pbc2       <- pbc[!is.na(pbc$trt), ]
pbc2$event <- as.integer(pbc$status[!is.na(pbc$trt)] == 2)
pbc2$sex_n <- as.integer(pbc2$sex == "f")

feat_cols <- c("trt","age","sex_n","ascites","hepato","spiders","edema",
               "bili","chol","albumin","copper","alk.phos","ast",
               "trig","platelet","protime","stage")

df <- pbc2[, c("time", "event", feat_cols)]
for (col in feat_cols)
  if (any(is.na(df[[col]])))
    df[[col]][is.na(df[[col]])] <- median(df[[col]], na.rm = TRUE)

set.seed(42)
idx_train <- sample(nrow(df), floor(0.75 * nrow(df)))
train <- df[idx_train, ]; test <- df[-idx_train, ]
X_tr  <- train[, feat_cols]
X_te  <- test[,  feat_cols]
y_te   <- Surv(test$time, test$event)

fit_boost_glmnet <- survivalisttoo::cox_gradient_boost(X_tr, train$time, train$event,
                            mlS3::wrap_glmnet, show_progress = FALSE, alpha=1)
print(glmnet::Cindex(predict(fit_boost_glmnet, X_te), y_te))

## [1] 0.8075089

fit_boost_svm <- survivalisttoo::cox_gradient_boost(X_tr, train$time, train$event,
                              mlS3::wrap_svm, show_progress = FALSE, kernel="radial")
print(glmnet::Cindex(predict(fit_boost_svm, X_te), y_te))

## [1] 0.772944

fit_boost_svm2 <- survivalisttoo::cox_gradient_boost(X_tr, train$time, train$event,
                           mlS3::wrap_svm, show_progress = FALSE, kernel="polynomial")
print(glmnet::Cindex(predict(fit_boost_svm2, X_te), y_te))

## [1] 0.7592372

fit_boost_caret <- survivalisttoo::cox_gradient_boost(X_tr, train$time, train$event,
                             mlS3::wrap_caret, show_progress = FALSE, method="enet")
print(glmnet::Cindex(predict(fit_boost_caret, X_te), y_te))

## [1] 0.7753278

fit_boost_caret <- survivalisttoo::cox_gradient_boost(X_tr, train$time, train$event,
                            mlS3::wrap_caret, show_progress = FALSE, method="enet",
                            fraction=0.5, lambda=0.01)
print(glmnet::Cindex(predict(fit_boost_caret, X_te), y_te))

## [1] 0.8122765

fit_boost_caret <- survivalisttoo::cox_gradient_boost(X_tr, train$time, train$event,
                             mlS3::wrap_caret, show_progress = FALSE, method="icr")
print(glmnet::Cindex(predict(fit_boost_caret, X_te), y_te))

## [1] 0.7991657

Introduction to R package survivalisttoo (with mlS3)