{"enrichment":{"faq":[{"a":"Lifelines is a Python survival analysis library built for medical research and epidemiology. It models time-to-event outcomes like disease progression or recovery, properly accounting for censored data from patients who leave studies early. Use it to estimate survival curves, compare treatment groups, and identify risk factors through Cox proportional hazards regression.","q":"What is lifelines and what does it do?"},{"a":"Lifelines is designed to properly handle censored time-to-event data in medical research by accounting for patients who leave studies early or whose events haven't occurred by the study end. Its core algorithms\u2014including Kaplan-Meier estimation and Cox regression\u2014incorporate censoring information directly, ensuring unbiased survival estimates even when follow-up is incomplete.","q":"How does lifelines handle censored data in time-to-event analysis?"},{"a":"Yes. Lifelines includes a Kaplan-Meier fitter that estimates and visualizes survival curves for clinical trials. It generates survival probability estimates over time and supports stratified analysis to compare outcomes between treatment groups, making it ideal for analyzing clinical trial survival data.","q":"Can lifelines perform Kaplan-Meier curves for clinical trials?"},{"a":"Lifelines' Cox proportional hazards model quantifies risk factors by computing hazard ratios\u2014the relative risk of an event between groups or per unit change in a covariate. Fit your time-to-event data with covariates, and the model outputs hazard ratios and confidence intervals to identify and measure which factors influence survival.","q":"How do I use Cox regression to identify hazard ratios in lifelines?"},{"a":"Lifelines supports logrank tests and other statistical comparisons to test whether survival curves differ significantly between treatment groups. These tests help determine if observed differences in survival outcomes are statistically meaningful in clinical trial data analysis.","q":"What statistical tests does lifelines provide for comparing survival outcomes?"},{"a":"Yes. Lifelines supports building predictive survival models for patient prognosis using Cox regression and parametric survival models like Weibull. These models estimate individual survival probabilities and can help clinicians assess disease progression and recovery risk.","q":"Is lifelines suitable for building predictive survival models?"}],"shadow_tags":["medical-statistics","time-to-event","biostatistics","clinical-research","hazard-modeling","epidemiological-analysis","censored-data","prognostic-modeling","survival-curves","parametric-distributions"],"summary_rewrite":"Lifelines is a Python survival analysis library built for medical research and epidemiology. It models time-to-event outcomes like disease progression or recovery, properly accounting for censored data from patients who leave studies early. Use it to estimate survival curves, compare treatment groups, and identify risk factors through Cox proportional hazards regression."},"files":[{"bytes":3086,"path":"skills/lifelines/SKILL.md","sha256":"d62c326db346cb7521134bf20eec0439f732d36a54e0dfa54b03e5d446ca609d","url":"https://skillfed.io/files/tondevrel/scientific-agent-skills/lifelines/dd3ff2b5/SKILL.md"}],"id":"tondevrel/scientific-agent-skills/lifelines","links":{"html":"https://skillfed.io/tondevrel/scientific-agent-skills/lifelines","md":"https://skillfed.io/tondevrel/scientific-agent-skills/lifelines.md","repo":"https://github.com/tondevrel/scientific-agent-skills"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":2,"language":null,"last_updated":"2026-02-01","license":"MIT","name":"lifelines","publisher":"tondevrel","stars":19},"relations":{"similar":[{"id":"aj-geddes/useful-ai-prompts/survival-analysis"},{"id":"beita6969/ScienceClaw/statistical-testing"},{"id":"jaechang-hits/SciAgent-Skills/scikit-survival-analysis"},{"id":"DrugClaw/DrugClaw/survival-analysis-tools"},{"id":"synthetic-sciences/openscience/scikit-survival"},{"id":"foryourhealth111-pixel/Vibe-Skills/scikit-survival"},{"id":"LeonChaoX/qinyan-academic-skills/scikit-survival"},{"id":"drshailesh88/integrated_content_OS/scikit-survival"},{"id":"beita6969/ScienceClaw/scikit-survival"},{"id":"mims-harvard/ToolUniverse/tooluniverse-statistical-modeling"}]},"slug":{"owner":"tondevrel","repo":"scientific-agent-skills","skill":"lifelines"},"version":"dd3ff2b5"}