Survival & Reliability calculator

Hazard Ratio and Cox regression calculator

Estimate Hazard Ratios (HR), 95% confidence intervals, and Cox proportional hazards regression parameters for survival time analysis.

Run this test live in StatLab npm i @statlab/core

When to use it

Use when comparing relative event rates over time while controlling for covariates.

Required Inputs

  • Survival times
  • Censoring status
  • Covariate groups (Treatment vs Control)

Mathematical Formula

h(t|X) = h₀(t) exp(βᵀX), HR = exp(β)

Reporting Cautions

  • Check proportional hazards assumption (Schoenfeld residuals).
  • HR < 1 indicates reduced hazard; HR > 1 indicates increased hazard.

Code Snippets (Python, R, TypeScript)

Python (SciPy / Statsmodels) 
from lifelines import CoxPHFitter
cph = CoxPHFitter()
cph.fit(df, duration_col='time', event_col='status')
print(cph.summary)
R Language 
library(survival)
coxph(Surv(time, status) ~ group, data = df)
TypeScript (@statlab/core) 
import { coxRegression } from '@statlab/core';
const res = coxRegression(df);

Developer Use Cases & Production Integrations

  • Quantifying relative crash risk reduction between deployment versions in VoxelAssurance.
  • Modeling component failure hazards under load.

VoxelPulse Telemetry Control Plane

Continuous A/B testing, statistical telemetry analysis, and automated feedback loops for live backend services.

Explore VoxelPulse →

VoxelAssurance Release Readiness

Fixed-price AI release-readiness sprints, automated regression testing, and reliability statistical benchmarks.

Explore VoxelAssurance →

Related Statistical Test Calculators

Explore static calculators across the StatLab inference engine suite.