Simulate a Node Using Cox-Regression

Data from the parents is used to generate the node using cox-regression using the method of Bender et al. (2005).

Usage

node_cox(data, parents, formula=NULL, betas, surv_dist, lambda, gamma,
         cens_dist, cens_args, name, as_two_cols=TRUE)

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). If supplied it should start with ~, having nothing else on the left hand side. The right hand side may contain any valid formula syntax, such as A + B or A + B + I(A^2), allowing non-linear effects. If this argument is defined, there is no need to define the parents argument. For example, using parents=c("A", "B") is equal to using formula= ~ A + B.
betas: A numeric vector with length equal to parents, specifying the causal beta coefficients used to generate the node.
surv_dist: A single character specifying the distribution that should be used when generating the survival times. Can be either "weibull" or "exponential".
lambda: A single number used as parameter defined by surv_dist.
gamma: A single number used as parameter defined by surv_dist.
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 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.

Details

The survival times are generated according to the cox proportional-hazards regression model as defined by the user. How exactly the data-generation works is described in detail in Bender et al. (2005). 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.

Unlike the other node type functions, this function usually adds two columns to the resulting dataset instead of one. The first column is called paste0(name, "_event") 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.

To simulate more complex time-to-event data, the user may need to use the sim_discrete_time function instead.

References

Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Statistics in Medicine. 2005; 24 (11): 1713-1723.

Author

Robin Denz

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 _event 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.

Examples

library(simDAG)

set.seed(3454)

# define DAG
dag <- empty_dag() +
  node("age", type="rnorm", mean=50, sd=4) +
  node("sex", type="rbernoulli", p=0.5) +
  node("death", type="cox", parents=c("sex", "age"), betas=c(1.1, 0.4),
       surv_dist="weibull", lambda=1.1, gamma=0.7, cens_dist="runif",
       cens_args=list(min=0, max=1))

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