Package 'recforest'

bladder1_recforest : Bladder Cancer Recurrences

Description

Preparation of the survival::bladder1 dataset for the recforest package. Please run ?survival::bladder1 for more information.

Usage

bladder1_recforest

Format

A data frame with 294 rows (118 individuals) and 8 variables:

id

Patient id

t.start

Start time

t.stop

Stop time

treatment

Placebo, pyridoxine (vitamin B6), or thiotepa

number

Initial number of tumors (8=8 or more)

size

Size (cm) of largest initial tumor

death

Death event

event

Recurrence event

Source

Script to generate the data can be explored using browseURL(system.file("generate_bladder1_recforest.R", package = "recforest"))

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Check if a Variable is Supported

Description

This function is a generic method that checks if a given variable is supported. The actual implementation of the check is provided by specific methods for different classes of variables.

Usage

is_supported_variable(x)

Arguments

x	The variable to be checked.

Value

A logical value indicating whether the variable is supported.

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Make a Decision Based on Input

Description

This function serves as a generic method for making decisions based on the input x and value. It dispatches to the appropriate method depending on the class of x.

Usage

make_decision(x, value)

Arguments

x	An object for which a decision needs to be made.
value	A value that influences the decision-making process.

Value

The result of the decision-making process, which depends on the specific method implementation.

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Plot method for recforest objects

Description

Plot method for recforest objects

Usage

## S3 method for class 'recforest'
plot(x, ...)

Arguments

x	An object of class recforest.
...	Additional arguments to be passed to the plot function.

Value

No return value, used for side effect.

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Predict using a recforest model

Description

This function generates predictions from a recforest model given a set of input features.

Usage

## S3 method for class 'recforest'
predict(
  object,
  newdata,
  id_var,
  covariates,
  time_vars = c("t.start", "t.stop"),
  death_var = NULL,
  ...
)

Arguments

object	A recforest model object.
newdata	A data frame containing the input features.
id_var	The name of the column containing the unique identifier for each subject.
covariates	A character vector containing the names of the columns to be used as predictors in the model.
time_vars	A length-2 character vector containing the names of the columns representing the start and stop times (default "t.start" and "t.stop").
death_var	The name of the column containing the death indicator or other any terminal event (optional).
...	Optional parameters to be passed to the low level function

Details

The predict_recforest function utilizes the ensemble of trees in the recforest model to generate predictions for new data. For each observation in newdata, the function aggregates the predictions from all trees in the recforest to provide a robust estimate.

Depending on the method specified during the initial training of the recforest model, the algorithm employs different prediction strategies:

• For standard recurrent event data, the function outputs the Nelson-Aalen estimates of the mean cumulative function.

• In the presence of terminal events, the function outputs the Ghosh-Lin estimates of the mean cumulative function.

The predictions represent the expected mean number of recurrent events for each individual at the end of the follow-up period.

Value

A vector of expected mean cumulative number of recurrent events per individual at the end of follow-up.

References

Cook, R. J., & Lawless, J. F. (1997). Marginal analysis of recurrent events and a terminating event. Statistics in medicine, 16(8), 911-924.

Ghosh, D., & Lin, D. Y. (2002). Marginal regression models for recurrent and terminal events. Statistica Sinica, 663-688.

Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests.

Examples

data("bladder1_recforest")
trained_forest <- train_forest(
  data = bladder1_recforest,
  id_var = "id",
  covariates = c("treatment", "number", "size"),
  time_vars = c("t.start", "t.stop"),
  death_var = "death",
  event = "event",
  n_trees = 2,
  n_bootstrap = 70,
  mtry = 2,
  minsplit = 3,
  nodesize = 15,
  method = "NAa",
  min_score = 5,
  max_nodes = 20,
  seed = 111,
  parallel = FALSE,
  verbose = FALSE
)
predictions <- predict(
 trained_forest,
 newdata = bladder1_recforest,
 id_var = "id",
 covariates = c("treatment", "number", "size"),
 time_vars = c("t.start", "t.stop"),
 death_var = "death"
)

---

Print method for recforest objects

Description

Print method for recforest objects

Usage

## S3 method for class 'recforest'
print(x, ...)

Arguments

x	An object of class recforest.
...	Additional arguments to be passed to the plot print

Value

Used for side effect. Will print to the console a description of each tree, with, for each:

• The number of nodes in the tree

• The metrics of the tree (c_index, mse_imse and mse_iscore)

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Summary Method for recforest Objects

Description

This function provides a summary of a recforest object by printing its metrics.

Usage

## S3 method for class 'recforest'
summary(object, ...)

Arguments

object	An object of class recforest.
...	Additional arguments to be passed to the summary function.

Value

The function prints the metrics of the recforest object.

---

Train a Recforest Model

Description

This function trains a recforest model using the provided data and parameters.

Usage

train_forest(
  data,
  id_var,
  covariates,
  event,
  time_vars = c("t.start", "t.stop"),
  death_var = NULL,
  n_trees,
  n_bootstrap = NULL,
  seed = NULL,
  mtry,
  minsplit,
  nodesize,
  method,
  min_score,
  max_nodes,
  parallel = FALSE,
  verbose = TRUE
)

Arguments

data	A data frame containing the dataset to be used for training the model.
id_var	The name of the column containing the unique identifier for each subject.
covariates	A character vector containing the names of the columns to be used as predictors in the model.
event	The name of the column containing the recurrent event indicator.
time_vars	A length-2 character vector containing the names of the columns representing the start and stop times (default "t.start" and "t.stop").
death_var	The name of the column containing the death indicator or other any terminal event (optional).
n_trees	The number of trees to be trained in the recforest model.
n_bootstrap	The number of bootstrap samples to be used for training each tree (in-bag sample). If not provided, it is set to 2/3 of the sample size (in term of number of unique id_var).
seed	An optional seed value to be used for reproducibility purpose (NULL by default).
mtry	The number of candidate variables randomly drawn at each node of the trees. This parameter should be tuned by minimizing the OOB error.
minsplit	The minimal number of events required to split the node. Cannot be smaller than 2.
nodesize	The minimal number of subjects required in both child nodes to split. Cannot be smaller than 1.
method	The method to be used for training the model. Currently, the following methods are supported : either "NAa" for Nelson-Aalen method, with no terminal event and no longitudinal time-dependent features; either "GL" for Ghosh-Lin modelization step with a terminal event and/or at least one longitudinal time-dependent feature.
min_score	The minimum score required to split a node. This parameter is used only when the method is set to "NAa".
max_nodes	The maximum number of nodes per tree.
parallel	A logical value indicating whether to use parallel processing for training the trees.
verbose	A logical value indicating whether to print progress messages.

Details

The recforest model aggregates predictions over an ensemble of trees, each constructed using a set of decision nodes based on specific splitting rules. At each node, a subset of predictors is randomly selected, and an optimal split is determined using an appropriate statistical test. Depending on the specified method, the algorithm employs different statistical tests to find the best split:

• For standard recurrent event data, the pseudo-score test statistic is used to compare two Nelson-Aalen estimates of the mean cumulative function.

• In the presence of terminal events and/or longitudinal variables, the Ghosh-Lin model is utilized to obtain the Wald test statistic, which provides a more accurate assessment of the split. The trees grow until they meet the stopping criteria, which include a minimum number of events (minsplit) and a minimum number of individuals in terminal nodes (nodesize). The final model is an ensemble of these trees, which helps to reduce overfitting and improve predictive performance by averaging the results on the out-of-bag sample.

Value

A list containing the following elements:

trees	A list of trained trees.
tree_metrics	A list of metrics for each tree.
metrics	A summary of the metrics for all trees.
columns	A list of column names used in the training.
params	A list of parameters used to set the model.
n_indiv	Number of individuals in the dataset.
n_predictors	Number of predictors used in the model.
n_trees	Number of trees trained.
n_bootstrap	Number of bootstrap samples used to grow each tree.
time	Computation time used to train the model.

References

Cook, R. J., & Lawless, J. F. (1997). Marginal analysis of recurrent events and a terminating event. Statistics in medicine, 16(8), 911-924.

Ghosh, D., & Lin, D. Y. (2002). Marginal regression models for recurrent and terminal events. Statistica Sinica, 663-688.

Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests.

Examples

data("bladder1_recforest")
trained_forest <- train_forest(
  data = bladder1_recforest,
  id_var = "id",
  covariates = c("treatment", "number", "size"),
  time_vars = c("t.start", "t.stop"),
  death_var = "death",
  event = "event",
  n_trees = 2,
  n_bootstrap = 70,
  mtry = 2,
  minsplit = 3,
  nodesize = 15,
  method = "NAa",
  min_score = 5,
  max_nodes = 20,
  seed = 111,
  parallel = FALSE,
  verbose = FALSE
)
print(trained_forest)
summary(trained_forest)

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