> For the complete documentation index, see [llms.txt](https://docs.owkin.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.owkin.com/what-you-can-do-with-k-pro/target-identification-and-indication-discovery/compare-groups.md).

# Compare groups

**What to do:** Define two patient subgroups and ask K-Pro to compare them across a specific data type. Be explicit about the outcome and the groups.

For survival comparisons, K-Pro reports Cox proportional-hazards results by default: hazard ratios with 95% confidence intervals and p-values, adjusted for multiple comparisons when there are more than two groups. It falls back to a log-rank test only if the Cox model cannot be fitted. For other comparisons, such as expression between two groups, you can name the test you want (for example a t-test or Wilcoxon test).

**Example prompt:**

> In the TCGA-LUAD cohort, test whether there is a statistically significant difference in overall survival between patients with and without a KRAS mutation. Report the hazard ratio, 95% confidence interval, and p-value.

**Expected result:** A Kaplan-Meier curve for the two groups, with the Cox hazard ratio, 95% CI, and p-value.

**Follow-up:**

> Run a multivariate Cox regression adjusting for age, tumor stage, and smoking status to check whether KRAS mutation status is an independent prognostic factor. Show the result as a forest plot.

**Expected result:** A multivariate forest plot showing the adjusted hazard ratio and 95% CI for each covariate.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.owkin.com/what-you-can-do-with-k-pro/target-identification-and-indication-discovery/compare-groups.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.