--- title: Survival Analysis and the Cox Proportional Hazards... description: A beginner-friendly walkthrough of survival analysis using the Rossi recidivism dataset in Python. It covers censoring, the survival function S(t) and hazard... canonical: https://daily.dev/posts/survival-analysis-and-the-cox-proportional-hazards-model-a-beginner-friendly-guide-ix1j5wqqw twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Survival Analysis and the Cox Proportional Hazards Model: A Beginner-Friendly Guide | daily.dev og:description: A beginner-friendly walkthrough of survival analysis using the Rossi recidivism dataset in Python. It covers censoring, the survival function S(t) and hazard... og:url: https://daily.dev/posts/survival-analysis-and-the-cox-proportional-hazards-model-a-beginner-friendly-guide-ix1j5wqqw og:image: https://api.daily.dev/og/posts/ix1j5WQQW.png og:image:alt: Survival Analysis and the Cox Proportional Hazards Model: A Beginner-Friendly Guide og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Survival Analysis and the Cox Proportional Hazards Model: A Beginner-Friendly Guide **[Towards Data Science](https://daily.dev/sources/tds)** · 14 min read · 0 upvotes · 0 comments ## Summary A beginner-friendly walkthrough of survival analysis using the Rossi recidivism dataset in Python. It covers censoring, the survival function S(t) and hazard function h(t), estimating survival curves with Kaplan-Meier, and fitting a Cox proportional hazards model with the lifelines library. Hazard ratios are interpreted for covariates like financial aid, age, and prior convictions, followed by a check of the proportional hazards assumption using Schoenfeld residuals, revealing violations for age and work experience. Remediation strategies (stratification, time-varying effects, functional form fixes) are explained alongside key takeaways about censoring, hazard ratios, and model diagnostics. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What does a hazard ratio of 0.68 mean in a Cox proportional hazards model? A hazard ratio of 0.68 means the covariate lowers the instantaneous rate of the event by 32% at every moment, relative to the baseline group. In the Rossi recidivism dataset, financial aid after prison release had an HR of 0.68 (95% CI 0.47-1.00, p=0.047), meaning aid recipients had a 32% lower hazard of re-arrest at any given time, though it does not indicate how many people avoided arrest overall. _daily.dev surfaces practical walkthroughs for developers applying statistical models like Cox regression in production._ ### How do I check the proportional hazards assumption in a Cox model using lifelines? Use the check_assumptions method on a fitted CoxPHFitter object, passing the dataframe and a p-value threshold, e.g. cph.check_assumptions(df, p_value_threshold=0.05, show_plots=True). This tests Schoenfeld residuals for a trend against time; a significant p-value indicates the covariate's effect changes over time, violating proportionality. In the Rossi dataset example, age and work experience (wexp) failed this test with p-values of 0.0007 and 0.0063. _developers debugging model assumptions can find hands-on lifelines examples curated on daily.dev._ ### What should I do when a covariate violates the proportional hazards assumption in a Cox model? Three remediation strategies exist: stratify on the offending variable by fitting a separate baseline hazard per level using the strata parameter, model the effect as time-varying with CoxTimeVaryingFitter, or check whether the covariate's true relationship is nonlinear (e.g. adding a squared term) since misspecified functional form can falsely trigger the Schoenfeld test. Stratifying on work experience in the Rossi dataset preserved the financial aid, age, and prior convictions effects. _comparing fixes for broken model assumptions gets easier with survival analysis guides collected on daily.dev._ ## Similar posts on daily.dev - [A Survival Analysis Guide with Python: Using Time-To-Event Models to Forecast Customer Lifetime](https://daily.dev/posts/a-survival-analysis-guide-with-python-using-time-to-event-models-to-forecast-customer-lifetime-c2y3qtmur) · Towards Data Science · 1 upvotes · 0 comments - [Survival Analysis for Data Drift and ML Reliability](https://daily.dev/posts/survival-analysis-for-data-drift-and-ml-reliability-jbgqpk5hl) · Towards Data Science · 0 upvotes · 0 comments - [Discrete Time-To-Event Modeling – Predicting When Something Will Happen](https://daily.dev/posts/discrete-time-to-event-modeling-predicting-when-something-will-happen-ymll5e6qc) · Towards Data Science · 0 upvotes · 0 comments - [Prognostic tool could help clinicians identify high-risk cancer patients](https://daily.dev/posts/prognostic-tool-could-help-clinicians-identify-high-risk-cancer-patients-4ixegusav) · MIT News · 0 upvotes · 0 comments - [Regression Modeling Strategies Short Course 2026, with Frank Harrell; May 14, 15, 18, 19](https://daily.dev/posts/regression-modeling-strategies-short-course-2026-with-frank-harrell-may-14-15-18-19-7ftw2wrqz) · R-bloggers · 0 upvotes · 0 comments --- Tags: [#python](https://daily.dev/tags/python), [#statistics](https://daily.dev/tags/statistics) [View this post on daily.dev](https://daily.dev/posts/survival-analysis-and-the-cox-proportional-hazards-model-a-beginner-friendly-guide-ix1j5wqqw) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Survival Analysis and the Cox Proportional Hazards Model: A Beginner-Friendly Guide","url":"https://daily.dev/posts/survival-analysis-and-the-cox-proportional-hazards-model-a-beginner-friendly-guide-ix1j5wqqw","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/survival-analysis-and-the-cox-proportional-hazards-model-a-beginner-friendly-guide-ix1j5wqqw"},"datePublished":"2026-08-23T17:09:42.674Z","dateModified":"2026-08-23T17:10:07.576Z","description":"A beginner-friendly walkthrough of survival analysis using the Rossi recidivism dataset in Python. 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In the Rossi recidivism dataset, financial aid after prison release had an HR of 0.68 (95% CI 0.47-1.00, p=0.047), meaning aid recipients had a 32% lower hazard of re-arrest at any given time, though it does not indicate how many people avoided arrest overall. daily.dev surfaces practical walkthroughs for developers applying statistical models like Cox regression in production."}},{"@type":"Question","name":"How do I check the proportional hazards assumption in a Cox model using lifelines?","acceptedAnswer":{"@type":"Answer","text":"Use the check_assumptions method on a fitted CoxPHFitter object, passing the dataframe and a p-value threshold, e.g. cph.check_assumptions(df, p_value_threshold=0.05, show_plots=True). This tests Schoenfeld residuals for a trend against time; a significant p-value indicates the covariate's effect changes over time, violating proportionality. 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