**Reusable code snippets and templates for working with PHS datasets**
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
This repository provides ready-to-use example code for common analytic tasks across the datasets available to our group — including claims databases on the [Stanford PHS Data Portal](https://stanfordphs.redivis.com/PHSData) and Epic's [Cosmos](https://cosmos.epic.com/) de-identified EHR database. Whether you're building a cohort, defining exposures and outcomes from claims, computing comorbidity indices, or querying EHR data in Redivis or Cosmos DSVM, you'll find tested, well-documented code here that you can adapt to your own projects.
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
> **Platforms**
> - **PHS Data Portal (Redivis):** Claims and EMR datasets queried via BigQuery SQL/Python/R/SAS in [Redivis](https://stanfordphs.redivis.com/PHSData) notebooks.
> - **Epic Cosmos DSVM:** De-identified EHR data (300M+ patients across 1,600+ hospitals) queried via SQL, R, or Python in Epic's [Data Science Virtual Machine](https://med.stanford.edu/starr-tools/other-resources/cosmos.html). DSVM access requires Epic certification (COS305 + COS500 + COS550). Note: no line-level data may be exported from Cosmos — only aggregate results after Epic review.
## Add your files
---
*[Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
*[Add files using the command line](https://docs.gitlab.com/topics/git/add_files/#add-files-to-a-git-repository) or push an existing Git repository with the following command:
Each dataset folder contains self-contained `.sql` (or `.py` / `.R`) scripts. Every script includes a header comment block describing its purpose, required input tables, output schema, and any important assumptions.
*[Set up project integrations](https://code.stanford.edu/afchealth/sample-codes/-/settings/integrations)
---
## Collaborate with your team
## What's Inside
*[Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
*[Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
*[Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
Below are examples of the types of analytic building blocks you'll find here. The list will grow as we contribute new code.
## Test and Deploy
**Cohort Construction**
- Continuous enrollment windows (commercial, Medicare, Medicaid)
- Age, sex, and plan-type filtering
- Index-date anchoring from diagnosis or procedure codes
Use the built-in continuous integration in GitLab.
**Comorbidity & Risk Scores**
- Charlson Comorbidity Index (CCI) — Deyo/Quan ICD-9/10 adaptation
- Elixhauser Comorbidity Index
*[Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/)
*[Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
*[Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
*[Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
*[Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
**Exposure & Outcome Definitions**
- Medication identification via NDC and procedure codes (e.g., opioids, MOUD)
- Diagnosis-based case definitions (ICD-9-CM / ICD-10-CM)
- Procedure-based episode construction (inpatient & outpatient)
***
**Claims Data Utilities**
- Length-of-stay calculation
- Censoring and follow-up time derivation
# Editing this README
**EHR / Cosmos Utilities**
- Patient cohort extraction from the Cosmos data model (EDDI)
- Lab result extraction and value-based phenotyping
- Medication order and administration queries
- etc.
---
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
1.**Browse** the folder for your dataset of interest.
2.**Copy** the relevant SQL, R, Python, SAS file into your Redivis project or notebook.
3.**Update** table references to point to your own Redivis project tables.
4.**Adapt** parameters (date ranges, code lists, lookback windows) to fit your study design.
## Name
Choose a self-explaining name for your project.
### For Epic Cosmos
## Description
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
1.**Browse** the `cosmos/` folder for the code you need.
2.**Open** the Cosmos DSVM and launch SQL, R, or Python as appropriate.
3.**Paste** the code and update table/view references to match the Cosmos data model (EDDI).
4.**Adapt** parameters to fit your study design.
5.**Remember:** No line-level data may leave the DSVM. Only aggregate results or images can be transferred after Epic's manual review. Mask small counts (< 11) and include the required Cosmos attribution in any publication.
## Badges
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
> **Tip:** Each script's header block lists every parameter you'll need to customize, marked with `-- TODO:` comments.
## Visuals
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
---
## Installation
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
## Contributing
## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
We encourage all team members and collaborators to contribute code. To add a new script:
## Support
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
1. Place it in the appropriate dataset folder (create one if needed).
2. Include a descriptive header comment block with:
-**Purpose** — what the code does
-**Platform** — Redivis (BigQuery SQL / R / Python / SAS) or Cosmos DSVM (SQL / R / Python)
-**Input** — required source tables and key fields
-**Output** — resulting table/columns
-**Assumptions** — any study-design or data-version assumptions
-**Author / Date** — for attribution and versioning
3. Open a merge request for review.
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
Please follow the existing naming and commenting conventions so the repository stays consistent and easy to navigate.
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
---
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
## Contact
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
Questions or suggestions? Open an issue in this repository or reach out to us via the team Slack channel.
## License
For open source projects, say how it is licensed.
---
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
*Maintained by the PHS Data Core team at the [Stanford Center for Population Health Sciences](https://med.stanford.edu/phs.html).*
