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In today’s post, we’ll see how to use rush and the probabilistic survival analysis API provided by techtonique.net (along with R and Python) to plot survival curves . Note that the web app also contains a page for plotting these curves, in 1 click. You can also read this post for more Python examples.
First, you’d need to install rush. Here is how I did it:
cd /Users/t/Documents/Python_Packages git clone https://github.com/jeroenjanssens/rush.git export PATH="/Users/t/Documents/Python_Packages/rush/exec:$PATH" source ~/.zshrc # or source ~/.bashrc rush --help # check if rush is installed
Now, download and save the following script in your current directory (note that there’s nothing malicious in it). Replace AUTH_TOKEN below by a token that can be found at techtonique.net/token:
Then, at the command line, run:
./2025-05-31-survival.sh
The result plot can be found in your current directory as a PNG file.
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Continue reading: Which patient is going to survive longer? Another guide to using techtonique dot net’s API (with R + Python + the command line) for survival analysis
Analysis of Using Rush and the Probabilistic Survival Analysis API with R and Python
The primary focus of the text is on using rush and the probabilistic survival analysis API provided by techtonique.net in conjunction with R and Python to plot survival curves. Other highlights of the text include the steps involved in installing ‘rush’ and utilizing it, as well as how to generate a result plot using these tools.
Long-Term Implications and Future Developments
Understanding survival analysis via multiple programming languages and tools like rush, R, and Python, combined with specific survival analysis API, streamlines critical data analysis tasks in healthcare, finance, and various other industries. In the long term, the ability to plot survival curves swiftly and conveniently can significantly enhance predictive decision-making processes.
At the core of such future developments is a continuous advancement in related technologies and tools. As the probabilistic survival analysis API of techtonique.net evolves, implementing more sophisticated algorithms, so will the tools used alongside, such as R, Python, and rush, to leverage these enhancements. We may anticipate significant improvements in predictive modelling and data visualization, that could lead to better survival analyses, amongst others.
Actionable Advice
Improving Skill Set
If you regularly deal with survival analysis, it would benefit you to become proficient in using multiple tools, including rush, R, and Python. Training yourself in these tools will increase your efficiency and expand your analytical capabilities. Consider taking some online courses or attending workshops to improve your know-how in using these programming languages.
Continuous Updates
Regularly update your version of rush, R, Python, and the probabilistic survival analysis API by techtonique.net to leverage the latest capabilities added. Ensure you update all these tools in a compatible manner to avoid any compatibility issues.
Validation of Scripts
Even though it was mentioned that “there’s nothing malicious” in the script provided in the text, it’s a good practice to always verify and validate any scripts before downloading and running them. This step ensures minimized risk to your data and systems.
Authenticity Check
Replace the AUTH_TOKEN used in the script with a token provided by techtonique.net. This precaution ensures the integrity of your operations and keeps your analysis accurate and authentic.