Upload time-to-event data, get hazard ratios and survival curves. Free.
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Running cox proportional hazards model analysis...
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Analyze another fileModels time-to-event data using Cox regression. Estimates how covariates affect the hazard rate — e.g., how patient characteristics influence time to recovery or how subscription features affect time to churn.
Use this when your outcome is time-to-event (survival time, time to churn, time to failure) and you have covariates.
If your outcome is binary (not time-based), use Logistic Regression. If you just need retention curves, use Cohort Retention.
Built for: Biostatistician, clinical researcher, reliability engineer, SaaS analyst
Typical data source: Time-to-event data with censoring indicators and covariates
Survival/time-to-event data
Minimum 50 rows · Best with 200-5000 observations
Performs Cox proportional hazards regression on time-to-event data. Estimates hazard ratios for covariates, produces forest plots, Kaplan-Meier survival curves, cumulative hazard plots, and tests the proportional hazards assumption with Schoenfeld residuals.
Cox regression coefficients with significance tests
HR > 1 increases hazard; HR < 1 is protective
Survival probability over time by group
Nelson-Aalen cumulative hazard estimate by group
Schoenfeld residual test — non-significant p-values support PH assumption
Concordance index, log-rank test, and model fit statistics
Plain-language summary of each covariate's effect on survival
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Cohort Retention — Just need retention curves without covariates
Need more power? Random Forest — Need non-linear survival modeling
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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