# MaxComboResult


The outcome of a MaxCombo weighted log-rank test.


Usage

``` python
MaxComboResult(
    statistic,
    p_value,
    z_statistics,
    correlation,
    method,
)
```


The MaxCombo test is designed to detect survival differences under non-proportional hazards by combining multiple Fleming-Harrington weight functions. The test statistic is the maximum absolute Z across the weight set, and the p-value accounts for the correlation between the Z-statistics.


## Attributes


`statistic: float`  
The MaxCombo test statistic: the maximum absolute Z-statistic across the weight functions.

`p_value: float`  
P-value from the multivariate normal distribution. The probability of observing a max \|Z\| this large or larger under the null hypothesis.

`z_statistics: dict[tuple[float, float], float]`  
Dictionary mapping each (rho, gamma) weight tuple to its Z-statistic.

`correlation: Array`  
Correlation matrix between the Z-statistics under H0, as a NumPy array.

`method: str`  
Human-readable description of the test.


## Examples

Run a MaxCombo test on the `lung` dataset, comparing survival by sex. The result holds the Z-statistics for each weight function and the combined p-value:


``` python
import greenwood as gw

lung = gw.load_dataset("lung", backend="polars")

mc = gw.maxcombo_test("Surv(time, status) ~ sex", data=lung)
mc
```


    MaxComboResult(statistic=3.5657, p_value=0.0009956, weights=[(0, 0), (1, 0), (0, 1), (1, 1)])


Inspect the per-weight Z-statistics:


``` python
mc.z_statistics
```


    {(0, 0): 3.21352484896035,
     (1, 0): 3.565690872910549,
     (0, 1): 1.8601032675878904,
     (1, 1): 2.7685344459841996}