Cox Proportional Hazards: The Workhorse of Survival Analysis
The Cox proportional hazards model is a key tool in survival analysis that allows researchers to assess the impact of various covariates on the risk of an event occurring without needing to specify the baseline hazard. This model is particularly useful in fields such as medicine and criminal justice, where understanding factors influencing outcomes like recidivism is crucial. The article discusses a study using the Rossi recidivism dataset to illustrate how the Cox model can identify significant predictors of rearrest among released prisoners.
- ▪The Cox model is semi-parametric and does not require assumptions about the baseline hazard.
- ▪In a study of 432 male prisoners, 26% were rearrested within a year of release.
- ▪Key findings included that older age and receiving financial aid reduced the risk of rearrest, while prior convictions increased it.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3843317) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Berkan Sesen Posted on May 18 • Originally published at sesen.ai Cox Proportional Hazards: The Workhorse of Survival Analysis #supervisedlearning #statistics Survival analysis starts with a question: how long until an event happens? A patient relapses, a customer churns, a borrower defaults on a loan, a prisoner is rearrested. Parametric models answer by assuming a shape for the hazard — Weibull, log-logistic, exponential — and estimating its parameters.
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