diff --git a/docs/disciplines/Bioinformatics/.pages b/docs/disciplines/Bioinformatics/.pages
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@@ -3,4 +3,5 @@ title: Bioinformatics
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   - index.md
   - 'Data Types': Data_Types
-  - 'Tools': Tools
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+  - 'Tools': Tools
+  - 'Methods': Methods
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diff --git a/docs/disciplines/Bioinformatics/Methods/survival_analysis.md b/docs/disciplines/Bioinformatics/Methods/survival_analysis.md
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+# Survival Analysis
+
+Survival analysis is a key statistical approach used to study time-to-event outcomes like time until disease progression, relapse, or death. Unlike traditional regression methods, survival analysis accounts for censoring (patients lost to follow-up or still alive at study end) and allows estimation of event probabilities over time.
+
+Survival analysis is often used to:
+
+- Evaluate the impact of potential biomarkers on treatment response and patient outcomes.
+
+- Compare survival curves across patient subgroups (e.g., different mutation profiles, treatments, methods in multicentre studies, etc.).
+
+- Identify predictive markers of drug benefit or toxicity.
+
+- Build models that can use multiomic data to guide precision medicine.
+
+Commonly used methods include:
+
+- **Kaplan–Meier curves**: non-parametric estimates of survival probabilities over time.
+
+- **Cox proportional hazards models**: regression framework to evaluate covariates (e.g., gene mutations, drug classes) affecting hazard rates.
+
+- **Random survival forests / survival support vector machines / deep learning survival models**: machine learning approaches that can capture complex interactions in high-dimensional omics datasets.
+
+Recommended Learning Resources
+
+- 🎥 **Video Playlist** - [Survival Analysis | Concepts and Implementation in R (YouTube)](https://www.youtube.com/playlist?list=PLqzoL9-eJTNDdnKvep_YHIwk2AMqHhuJ0)
+
+    Great introduction to both fundamentals and applied examples in R.
+
+- 📘 **Tutorial in R** – [Survival Analysis in R](https://www.emilyzabor.com/survival-analysis-in-r.html) [| Slide](https://www.emilyzabor.com/talks.html)
+
+    Hands-on guidance with code snippets.
+
+- 📄 **Articles** 
+    * [An Introduction to Survival Statistics: Kaplan–Meier Analysis (PMC)](https://pmc.ncbi.nlm.nih.gov/articles/PMC5045282/)  
+        Clear explanation of Kaplan–Meier methodology with biomedical applications.
+    * [Survival Analysis Part I: Basic concepts and first analyses](https://pmc.ncbi.nlm.nih.gov/articles/PMC2394262/)  
+      [Survival Analysis Part II: Multivariate data analysis – an introduction to concepts and methods](https://pmc.ncbi.nlm.nih.gov/articles/PMC2394368/)  
+      [Survival Analysis Part III: Multivariate data analysis – choosing a model and assessing its adequacy and fit](https://pmc.ncbi.nlm.nih.gov/articles/PMC2376927/)  
+      [Survival Analysis Part IV: Further concepts and methods in survival analysis](https://pmc.ncbi.nlm.nih.gov/articles/PMC2394469/)  
+        A four-part series covering basic methods and terminology in survival analysis. Frequently asked questions are addressed in Part IV.