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Running heart failure survival analysis analysis...
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Analyze another fileKaplan-Meier survival curves and multivariable Cox proportional hazards regression identify independent mortality predictors in heart failure patients, with time-dependent AUC validation
Use when you have time-to-event data with censoring and want to identify clinical risk factors for mortality or other outcomes
Do not use if events are rare (fewer than 10), follow-up is too short, or covariates are highly collinear without adjustment
Built for: Biostatisticians, clinical data analysts, clinical researchers, epidemiologists, health data scientists, and medical researchers
Typical data source: Patient cohort records with follow-up time, event indicator (death/censored), and clinical covariates such as age, lab values, and comorbidity flags
Dataset with 13 columns
Minimum 30 rows
Heart Failure Clinical Records (Kaggle, 299 patients, 13 variables) tracks follow-up time and mortality outcome alongside clinical measurements. Build a full survival analysis report: overall Kaplan-Meier survival curve, stratified curves by key risk factors, Cox proportional hazards model with hazard ratios + CIs, proportional hazards assumption checks, and a time-dependent ROC.
Overall Kaplan-Meier survival curve with 95% confidence interval
Kaplan-Meier survival curves stratified by patient sex
Kaplan-Meier survival curves for binary risk factors (anaemia, diabetes, hypertension, smoking)
Log-rank test p-values for binary clinical covariates
Cox model hazard ratios with confidence intervals for all predictors
Complete Cox regression coefficient table with statistics
Schoenfeld residuals over time to assess proportional hazards assumption
Time-dependent AUC at 30, 90, and 180-day landmarks
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 identify which factors predict a binary outcome and follow-up time is not available or not relevant -- logistic regression without a time component
Need more power? Propensity Score Matching — When your cohort is observational and you need to control for treatment-selection bias before comparing survival outcomes between groups
Similar: Survival Analysis, Stroke Risk Factors
Clinical risk stratification
Clinical risk stratification
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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