From 2fe1254cf86a4c60da4b2d26e029af63e734b87e Mon Sep 17 00:00:00 2001 From: Guanqiao Feng Date: Tue, 26 Aug 2025 11:40:46 -0400 Subject: [PATCH 1/4] added survival analysis under disciplines/pharmacogenomics --- .../Pharmacogenomics/survival_analysis.md | 35 +++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 docs/disciplines/Pharmacogenomics/survival_analysis.md diff --git a/docs/disciplines/Pharmacogenomics/survival_analysis.md b/docs/disciplines/Pharmacogenomics/survival_analysis.md new file mode 100644 index 000000000..bb84c377a --- /dev/null +++ b/docs/disciplines/Pharmacogenomics/survival_analysis.md @@ -0,0 +1,35 @@ +# Survival Analysis + +Survival analysis is a key statistical approach used to study time-to-event outcomes—for example, 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. + +In the **pharmacogenomics field**, survival analysis is often used to: + +- Evaluate the impact of genomic biomarkers on treatment response and patient outcomes. + +- Compare survival curves across patient subgroups (e.g., different mutation profiles). + +- Identify predictive markers of drug benefit or toxicity. + +- Build models that combine molecular, clinical, and drug-response data to guide precision medicine. + +Commonly used methods include: + +- **Kaplan–Meier** curves: non-parametric estimates of survival probabilities over time. + +- **Cox proportional hazards model**: regression framework to evaluate covariates (e.g., gene mutations, drug classes) affecting hazard rates. + +- **Random survival forests / 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. + +- 📄 **Article** – [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. \ No newline at end of file From 4c92dae876e7b946db20c78a4b40088c55204962 Mon Sep 17 00:00:00 2001 From: kaitlyn-kobayashi Date: Wed, 17 Dec 2025 18:56:19 +0000 Subject: [PATCH 2/4] chore: add methods subtab to bioinformatics --- docs/disciplines/Bioinformatics/.pages | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/disciplines/Bioinformatics/.pages b/docs/disciplines/Bioinformatics/.pages index d4c1345ed..907e5ad45 100644 --- a/docs/disciplines/Bioinformatics/.pages +++ b/docs/disciplines/Bioinformatics/.pages @@ -3,4 +3,5 @@ title: Bioinformatics nav: - index.md - 'Data Types': Data_Types - - 'Tools': Tools \ No newline at end of file + - 'Tools': Tools + - 'Methods': Methods \ No newline at end of file From b75e74dc77ac09b866c2d6ac0d1f8f0f6a7c935c Mon Sep 17 00:00:00 2001 From: kaitlyn-kobayashi Date: Wed, 17 Dec 2025 18:57:27 +0000 Subject: [PATCH 3/4] docs: move survival analysis to bioinformatics with language generalization and resources updates --- .../Methods/survival_analysis.md | 40 +++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 docs/disciplines/Bioinformatics/Methods/survival_analysis.md diff --git a/docs/disciplines/Bioinformatics/Methods/survival_analysis.md b/docs/disciplines/Bioinformatics/Methods/survival_analysis.md new file mode 100644 index 000000000..1d0e29054 --- /dev/null +++ b/docs/disciplines/Bioinformatics/Methods/survival_analysis.md @@ -0,0 +1,40 @@ +# 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. From 9abdfdafac263fcf3dedb11cedb7ba6114ceffb0 Mon Sep 17 00:00:00 2001 From: kaitlyn-kobayashi Date: Wed, 17 Dec 2025 18:57:54 +0000 Subject: [PATCH 4/4] chore: remove survival analysis from pharmacogenomics subtab --- .../Pharmacogenomics/survival_analysis.md | 35 ------------------- 1 file changed, 35 deletions(-) delete mode 100644 docs/disciplines/Pharmacogenomics/survival_analysis.md diff --git a/docs/disciplines/Pharmacogenomics/survival_analysis.md b/docs/disciplines/Pharmacogenomics/survival_analysis.md deleted file mode 100644 index bb84c377a..000000000 --- a/docs/disciplines/Pharmacogenomics/survival_analysis.md +++ /dev/null @@ -1,35 +0,0 @@ -# Survival Analysis - -Survival analysis is a key statistical approach used to study time-to-event outcomes—for example, 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. - -In the **pharmacogenomics field**, survival analysis is often used to: - -- Evaluate the impact of genomic biomarkers on treatment response and patient outcomes. - -- Compare survival curves across patient subgroups (e.g., different mutation profiles). - -- Identify predictive markers of drug benefit or toxicity. - -- Build models that combine molecular, clinical, and drug-response data to guide precision medicine. - -Commonly used methods include: - -- **Kaplan–Meier** curves: non-parametric estimates of survival probabilities over time. - -- **Cox proportional hazards model**: regression framework to evaluate covariates (e.g., gene mutations, drug classes) affecting hazard rates. - -- **Random survival forests / 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. - -- 📄 **Article** – [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. \ No newline at end of file