| Name | pyicare JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | |
| Summary | A Python distribution of iCARE, a tool for individualized Coherent Absolute Risk Estimation. |
| upload_time | 2023-05-25 16:02:31 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
gwas
webassembly
survival-analysis
cox-proportional-hazard
genome-wide-association-studies
absolute-risk
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Py-iCARE
**Py-iCARE** is a Python distribution of iCARE, a tool for **i**ndividualized **C**oherent **A**bsolute **R**isk **E**stimation. iCARE allows users to build, validate, and apply absolute risk models. [Absolute risk](https://www.cancer.gov/publications/dictionaries/cancer-terms/def/absolute-risk) quantifies the chance of an event occurring. For example, the likelihood that a healthy individual, of a given age and a risk factor profile, will develop the disease of interest over a specified time interval.
The original iCARE was written in R and its archived version (1.26.0) is available via Bioconductor at: https://www.bioconductor.org/packages/release/bioc/html/iCARE.html.
## Motivation
The main motivation of porting iCARE from R to Python was to enable its use as a [WebAssembly](https://webassembly.org/) module (via [Pyodide](https://pyodide.org/en/latest/index.html)) for the proliferation of portable and privacy-preserving web applications that can build, validate, and apply absolute risk models. Python also enables researchers to leverage its rapidly evolving data science ecosystem— including [TensorFlow](https://www.tensorflow.org/), [PyTorch](https://pytorch.org/), [statsmodels](https://www.statsmodels.org/stable/index.html), and [scikit-learn](https://scikit-learn.org/stable/)— to explore novel absolute risk models that can incorporate evidence from wider sources of data.
## Installation
This repository contains a Python package. It can be installed via PyPI as shown below. It is also compiled into WebAssembly via Pyodide. The wrapper JavaScript library, as ES6 modules, is also distributed in this repository at GitHub Pages. It can be accessed by any JavaScript runtime environment, including Node.js, web browsers, and Quarto notebooks in RStudio (to interface with R, Julia, and/or Python). Specifically, the steps to access it via JavaScript and R are also shown below.
### Python
If you want to access iCARE from a purely Python runtime environment, you can install it via PyPI:
```bash
pip install pyicare
```
iCARE is supported on Python 3.7 and above.
### JavaScript
ES6 import JS SDK via GitHub Pages.
### R
ES6 import JS SDK via Quarto.
## Usage
Once installed, Py-iCARE can be imported into your Python scripts as follows:
```python
import icare
```
Py-iCARE is a library with three main functions: 1) `compute_absolute_risk()`, a method to build and apply absolute risk models; 2) `compute_absolute_risk_split_interval()`, a method to build and apply absolute risk models that relaxes the proportional hazard assumption to some extent by allowing you to specify different model parameters before and after a cut-point in time; and 3) `validate_absolute_risk_model()`, a method to validate absolute risk models on an independent cohort study data or a case-control study nested within a cohort.
Example usages of these functions are shown in Jupyter notebooks at the [examples/Python](https://github.com/jeyabbalas/py-icare/tree/master/examples/Python) directory.
### R
Quarto
### JavaScript
ES6 import into script
## License
Py-iCARE is open-source licensed under the MIT License.
## References
1. [Pal Choudhury, Parichoy, Paige Maas, Amber Wilcox, William Wheeler, Mark Brook, David Check, Montserrat Garcia-Closas, and Nilanjan Chatterjee. "iCARE: an R package to build, validate and apply absolute risk models." PloS one 15, no. 2 (2020): e0228198.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001949/)
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