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Analyze another fileKaplan-Meier survival analysis and Cox proportional hazards regression to identify clinical predictors of time-to-death in heart failure patients
Use when you have time-to-event data with censoring and want to identify which covariates affect survival probability
Do not use if outcome is binary without time component, or if follow-up is too short to observe meaningful events
Built for: Clinical researchers, cardiologists, biostatisticians, epidemiologists, cardiology fellows, medical data scientists
Typical data source: Patient-level heart failure registry data with follow-up time in days, a binary death event indicator, and clinical variables such as ejection fraction, serum creatinine, anaemia status, age, and comorbidities
Dataset with 13 columns
Minimum 50 rows
Cornerstone #16 — Kaplan-Meier + Cox proportional hazards on heart failure (3,163 votes)
Kaplan-Meier overall survival probability curve showing time-to-death distribution across the follow-up period
Stratified KM curves by ejection fraction group (Low/Borderline/Normal) with log-rank test significance
Stratified KM curves comparing anaemic vs non-anaemic patients with log-rank test significance
Cox proportional hazards model hazard ratios for all clinical predictors after multivariate adjustment
Scatter plot of serum creatinine vs ejection fraction colored by survival outcome
Statistical comparison of key clinical characteristics between patients who died vs survived
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Diabetes Risk Drivers — When you only need to predict a binary outcome (survived vs. died) without accounting for when the event occurred or censored observations -- logistic regression ignores time but is simpler to interpret
Need more power? Cancer Classification — When you need to classify patients into multiple diagnostic categories rather than model time-to-event outcomes -- uses machine learning classifiers instead of Cox regression
Similar: Stroke Risk Factors, Attrition Drivers
Upload your heart failure registry with follow-up time and death event indicator. The Cox proportional hazards model ranks all clinical predictors by adjusted hazard ratio, showing which factors independently increase mortality risk after controlling for confounders.
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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