Internal helper function to easily return the correct survival predict types.

## Usage

```
.surv_return(
times = NULL,
surv = NULL,
crank = NULL,
lp = NULL,
response = NULL,
which.curve = NULL
)
```

## Arguments

- times
(

`numeric()`

)

Vector of survival times.- surv
(

`matrix()|array()`

)

Matrix or array of predicted survival probabilities, rows (1st dimension) are observations, columns (2nd dimension) are times and in the case of an array there should be one more dimension. Number of columns should be equal to length of`times`

. In case a`numeric()`

vector is provided, it is converted to a single row (one observation) matrix.- crank
(

`numeric()`

)

Relative risk/continuous ranking. Higher value is associated with higher risk. If`NULL`

then either set as`-response`

if available or`lp`

if available (this assumes that the`lp`

prediction comes from a PH type model - in case of an AFT model the user should provide`-lp`

). In case neither`response`

or`lp`

are provided, then`crank`

is calculated as the sum of the cumulative hazard function (**expected mortality**) derived from the predicted survival function (`surv`

), see get_mortality. In case`surv`

is a 3d array, we use the`which.curve`

parameter to decide which survival matrix (index in the 3rd dimension) will be chosen for the calculation of`crank`

.- lp
(

`numeric()`

)

Predicted linear predictor, used to impute`crank`

if`NULL`

.- response
(

`numeric()`

)

Predicted survival time, passed through function without modification.- which.curve
Which curve (3rd dimension) should the

`crank`

be calculated for, in case`surv`

is an`array`

? If between (0,1) it is taken as the quantile of the curves otherwise if greater than 1 it is taken as the curve index. It can also be 'mean' and the survival probabilities are averaged across the 3rd dimension. Default value (`NULL`

) is the**0.5 quantile**which is the median across the 3rd dimension of the survival array.

## References

Sonabend, Raphael, Bender, Andreas, Vollmer, Sebastian (2022).
“Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures.”
*Bioinformatics*.
ISSN 1367-4803, doi:10.1093/BIOINFORMATICS/BTAC451
, https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btac451/6640155.