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https://github.com/eurobios-mews-labs/survival-data-handler
https://github.com/eurobios-mews-labs/survival-data-handler
Last synced: 8 days ago
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- Host: GitHub
- URL: https://github.com/eurobios-mews-labs/survival-data-handler
- Owner: eurobios-mews-labs
- License: apache-2.0
- Created: 2024-04-16T07:39:00.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-10-09T09:27:38.000Z (3 months ago)
- Last Synced: 2024-11-08T09:06:41.357Z (2 months ago)
- Language: Python
- Size: 657 KB
- Stars: 0
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Survival data handler
[](https://badge.fury.io/py/palma)
The aim of this package is to facilitate the use of survival data by switching from temporal data in the form of a
collection of survival functions to temporal matrices calculating other functions derived from survival analysis,
such as residual life, hazard function, etc. analysis, such as residual life expectancy, hazard function, etc.
### Install
```shell
pip install survival-data-handler
```
### Usage
```python
import pandas as pd
from lifelines import CoxPHFitter
from lifelines.datasets import load_rossi
rossi = load_rossi()
cph = CoxPHFitter()
cph.fit(rossi, duration_col='week', event_col='arrest')
curves = cph.predict_survival_function(rossi).T
curves.columns = pd.to_timedelta(curves.columns.to_numpy() * 7, unit="D")
print(curves.head())
```
| | 7 days 00:00:00 | 14 days 00:00:00 | 21 days 00:00:00 | 28 days 00:00:00 | 35 days 00:00:00 | 42 days 00:00:00 | 49 days 00:00:00 | 56 days 00:00:00 | 63 days 00:00:00 | 70 days 00:00:00 |
|---:|------------------:|-------------------:|-------------------:|-------------------:|-------------------:|-------------------:|-------------------:|-------------------:|-------------------:|-------------------:|
| 0 | 0.997616 | 0.99523 | 0.992848 | 0.990468 | 0.988085 | 0.985699 | 0.983305 | 0.971402 | 0.966614 | 0.964223 |
| 1 | 0.993695 | 0.987411 | 0.981162 | 0.974941 | 0.968739 | 0.962552 | 0.95637 | 0.926001 | 0.913958 | 0.907978 |
| 2 | 0.994083 | 0.988183 | 0.982314 | 0.976468 | 0.970639 | 0.96482 | 0.959004 | 0.930402 | 0.919043 | 0.913399 |
| 3 | 0.999045 | 0.998089 | 0.997133 | 0.996176 | 0.995216 | 0.994254 | 0.993287 | 0.98846 | 0.986508 | 0.985531 |
| 4 | 0.997626 | 0.99525 | 0.992878 | 0.990507 | 0.988135 | 0.985758 | 0.983374 | 0.97152 | 0.966752 | 0.96437 |
```python
from survival_data_handler import Lifespan
age = pd.to_timedelta(rossi["age"] * 365.25, unit="D")
birth = pd.to_datetime('2000')
rossi["index"] = rossi.index
birth = pd.to_datetime('2000')
lifespan = Lifespan(
curves,
index=rossi["index"],
birth=birth,
age=age,
window=(pd.to_datetime("2000"), pd.to_datetime("2001"))
)
```
We now add the supervision data (in the form of duration)
```python
lifespan.add_supervision(
event=rossi["arrest"], # True if the data is observed False, when censored
durations=rossi["duration"] + birth # The duration
)
```
Let's calculate the associated performance
```python
lifespan.assess_metric("survival_function")
```
| Date | Performance (1 - AUC) |
|------------|-----------------------|
| 2000-01-31 | 0.468458 |
| 2000-03-01 | 0.384425 |
| 2000-03-31 | 0.432012 |
| 2000-04-30 | 0.357338 |
| 2000-05-30 | 0.365263 |
| 2000-06-29 | 0.365190 |
| 2000-07-29 | 0.371438 |
| 2000-08-28 | 0.343447 |
| 2000-09-27 | 0.340607 |
| 2000-10-27 | 0.344628 |
| 2000-11-26 | 0.334398 |
| 2000-12-26 | 0.334444 |