# concordance_index_ci()


IPCW concordance index with a confidence interval.


Usage

``` python
concordance_index_ci(
    surv,
    risk,
    *,
    data=None,
    cause=None,
    tau=None,
    conf_level=0.95,
    transform="logit",
)
```


Computes the Uno-style IPCW concordance and derives the standard error from the influence function decomposition. The confidence interval is constructed on the logit (default), log, or identity scale and back-transformed.


## Parameters


`surv: Surv | Outcome | str`  
A [Surv](Surv.md#greenwood.Surv) response. If `cause` is given, must be multi-state ([Surv()](Surv.md#greenwood.Surv) with a categorical event). Otherwise it must be right-censored (`Surv(time=..., event=...)`). An [Outcome](Outcome.md#greenwood.Outcome) or a formula response such as `'Surv(time, status == 2)'` is also accepted, with its columns read from `data`.

`risk: Any`  
Risk score or predicted cumulative incidence, one per subject.

`cause: int | None = None`  
Integer cause code for the competing-risks concordance. `None` (default) uses the standard right-censored concordance.

`tau: float | None = None`  
Truncation time. Defaults to the largest observed (cause-specific) event time.

`conf_level: float = ``0.95`  
Confidence level for the interval (default 0.95).

`transform: str = ``"logit"`  
Scale for the CI: `"logit"` (default, recommended), `"log"`, or `"identity"`.

`data: Any = None`  
A data frame (pandas, Polars, PyArrow, DuckDB, a lazy frame, …) holding the columns named by the response and `risk`. When `surv` is an [Outcome](Outcome.md#greenwood.Outcome) or a formula, rows with a missing value in any column used are dropped first, along with the matching rows of any arrays passed alongside.


## Returns


`ConcordanceResult`  
Point estimate, standard error, and confidence limits.


## Examples


``` python
import greenwood as gw

lung = gw.load_dataset("lung", backend="polars")
death = gw.Outcome.surv(time="time", event="status")
cox = gw.CoxPH().fit(death, covariates=["age", "sex"], data=lung)
lp = cox.predict(type="lp")

gw.concordance_index_ci(death, risk=lp, data=lung)
```


    ConcordanceResult(estimate=0.5959, se=0.0391, CI=[0.5176, 0.6696], conf_level=0.95)