node_aalen: Generate Data from an Aalen Additive Hazards Model

In simDAG: Simulate Data from a (Time-Dependent) Causal DAG

Generate Data from an Aalen Additive Hazards Model

Description

Data from the parents is used to generate the node using Aalen additive hazards regression using the inversion method. Currently, only time-constant coefficients and a constant baseline hazard function are supported.

Usage

node_aalen(data, parents, formula=NULL, betas, intercept,
           cens_dist=NULL, cens_args, name,
           as_two_cols=TRUE, left=0)

Arguments

data	A data.table (or something that can be coerced to a data.table) containing all columns specified by parents.
parents	A character vector specifying the names of the parents that this particular child node has. If non-linear combinations or interaction effects should be included, the user may specify the formula argument instead.
formula	An optional formula object to describe how the node should be generated or NULL (default). This argument only works if the function is used as a node type in a node call. See ?node or the associated vignette for more information about how the formula argument should be specified in this package.
betas	A numeric vector with length equal to parents, specifying the causal beta coefficients used to generate the node.
intercept	A single number, specifying the intercept of the model.
cens_dist	A single character naming the distribution function that should be used to generate the censoring times or a suitable function. For example, "runif" could be used to generate uniformly distributed censoring times. Set to NULL (default) to get no censoring.
cens_args	A list of named arguments which will be passed to the function specified by the cens_dist argument.
name	A single character string specifying the name of the node.
as_two_cols	Either TRUE or FALSE, specifying whether the output should be divided into two columns. When cens_dist is specified, this argument will always be treated as TRUE because two columns are needed to encode both the time to the event and the status indicator. When no censoring is applied, however, users may set this argument to FALSE to simply return the numbers as they are.
left	A single number, specifying the left-truncation time. If set to something > 0, only times that are larger than this value will be generated. Is set to 0 by default, so that no left-truncation is used.

Details

This function generates survival times according to a Aalen additive hazards model with time-constant beta coefficients and a time-constant baseline hazard. Time-dependent effects or time-dependent baseline hazards are currently not supported. To also include censoring, this function allows the user to supply a function that generates random censoring times. If the censoring time is smaller than the generated survival time, the individual is considered censored.

Like the other time-to-event based node type functions, this function usually adds two columns to the resulting dataset instead of one. The first column is called paste0(name, "_status") and is a logical variable, where TRUE indicates that the event has happened and FALSE indicates right-censoring. The second column is named paste0(name, "_time") and includes the survival or censoring time corresponding to the previously mentioned event indicator. This is the standard format for right-censored time-to-event data without time-varying covariates. If no censoring is applied, this behavior can be turned off using the as_two_cols argument.

Value

Returns a data.table of length nrow(data) containing two columns if as_two_cols=TRUE and always when cens_dist is specified. In this case, both columns start with the nodes name and end with _status and _time. The first is a logical vector, the second a numeric one. If as_two_cols=FALSE and cens_dist is NULL, a numeric vector is returned instead.

Author(s)

Robin Denz

References

Aalen, Odd O. A Linear Regression Model for the Analysis of Life Times. Statistics in Medicine. 1989; (8): 907-925.

Examples

library(simDAG)

set.seed(34543)

# define DAG, here with two baseline covariates and
# no censoring of Y
dag <- empty_dag() +
  node("A", type="runif") +
  node("B", type="rbernoulli") +
  node("Y", type="aalen", formula= ~ 0.1 + A*0.2 + B*-0.05)

sim_dat <- sim_from_dag(dag=dag, n_sim=1000)
head(sim_dat)

simDAG documentation built on Jan. 9, 2026, 1:08 a.m.