Stores metadata related to the ID variables used to index input_mats and transformed parameter objects already predicted from covariates.

```
id_attributes(
strategy_id,
n_strategies,
patient_id,
n_patients,
state_id = NULL,
n_states = NULL,
transition_id = NULL,
n_transitions = NULL,
time_id = NULL,
time_intervals = NULL,
n_times = NULL,
sample = NULL,
n_samples = NULL,
grp_id = NULL,
patient_wt = NULL
)
```

- strategy_id
A numeric vector of integers denoting the treatment strategy.

- n_strategies
A scalar denoting the number of unique treatment strategies.

- patient_id
A numeric vector of integers denoting the patient.

- n_patients
A scalar denoting the number of unique patients.

- state_id
A numeric vector of integers denoting the health state.

- n_states
A scalar denoting the number of unique health states.

- transition_id
A numeric vector denoting the health state transition. This is only used for state transition models.

- n_transitions
A scalar denoting the number of unique transitions.

- time_id
A numeric vector of integers denoting a unique time interval.

- time_intervals
A

`data.table`

denoting unique time intervals. Must contain the columns`time_id`

,`time_start`

, and`time_stop`

.`time_start`

is the starting time of an interval and`time_stop`

is the stopping time of an interval. Following the survival package, time intervals are closed on the right and open on the left (except in the final interval where`time_stop`

is equal to infinity).- n_times
A scalar denoting the number of time intervals. Equal to the number of rows in

`time_intervals`

.- sample
A numeric vector of integer denoting the sample from the posterior distribution of the parameters.

- n_samples
A scalar denoting the number of samples.

- grp_id
An optional numeric vector of integers denoting the subgroup.

- patient_wt
An optional numeric vector denoting the weight to apply to each patient within a subgroup.

When using the ID variables to index input_mats, the sorting order
should be the same as specified in `expand.hesim_data()`

; that is,
observations must be sorted by prioritizing: (i) `strategy_id`

, (ii) `patient_id`

,
(iii) the health-related ID variable (either `state_id`

or `transition_id`

),
and (iv) `time_id`

. When using ID variables are used to index transformed parameter
objects and `sample`

is used for indexing, then observations must be sorted by
prioritizing: (i) `sample`

, (ii) `strategy_id`

, (iii) `patient_id`

,
(iv) the health-related ID variable, and (v) `time_id`

.