# SimulatedCompetingRisks


Result of [simulate_competing_risks()](simulate_competing_risks.md#greenwood.simulate_competing_risks).


Usage

``` python
SimulatedCompetingRisks(
    surv,
    covariates,
    latent_times,
    censoring_time,
)
```


Bundles the simulated survival response, covariate matrix, and latent ground-truth times into a single object. This makes it easy to pass synthetic data directly to competing-risks estimators and metrics.


## Attributes


`surv: Surv`  
A multi-state [Surv](Surv.md#greenwood.Surv) response ready for use with [AalenJohansen](AalenJohansen.md#greenwood.AalenJohansen), [FineGray](FineGray.md#greenwood.FineGray), or any competing-risks metric.

`covariates: Array | None`  
Covariate matrix of shape `(n, n_covariates)`, or `None` when `n_covariates=0`.

`latent_times: Array`  
Per-cause latent event times of shape `(n, n_causes)` before the competing-risks minimum is taken. Useful for constructing oracle predictions in tests and benchmarks.

`censoring_time: Array`  
The censoring time for each subject, shape `(n,)`. The observed time is `min(min(latent_times, axis=1), censoring_time)`.


## Examples

Generate synthetic competing-risks data with two causes and three covariates. The result holds a ready-to-use [Surv](Surv.md#greenwood.Surv) response and covariate matrix:


``` python
import greenwood as gw

sim = gw.simulate_competing_risks(
    n=500, n_causes=2, n_covariates=3, seed=42
)
sim.surv
```


    Surv(type=mright, n=500, events=296, states=('cause1', 'cause2'))


The covariates and latent times are available for oracle comparisons:


``` python
sim.covariates.shape, sim.latent_times.shape
```


    ((500, 3), (500, 2))