Cox Proportional Hazards Regression in MedCalc

Introduction

Survival analysis is a crucial statistical method in biomedical research used to analyze time-to-event data such as patient survival time, disease recurrence, or treatment outcomes. One of the most powerful models in survival analysis is the Cox Proportional Hazards Regression.

In this tutorial, we explain how to perform Cox regression using MedCalc software, interpret results, and understand survival curves using real biomedical data.

What is Cox Proportional Hazards Regression?

Cox regression is a semi-parametric model used to evaluate the effect of multiple variables on survival time. It estimates the hazard ratio (HR), which indicates how a predictor variable affects the risk of an event occurring.

Key Concept

• Hazard = Risk of event at a given time
• Hazard Ratio (HR):
  • HR > 1 → Increased risk
  • HR < 1 → Decreased risk
  • HR = 1 → No effect

Example Dataset (Biomedical)

👉 Coding:

• Status → 1 = Event, 0 = Censored
• Treatment → 1 = Drug A, 2 = Drug B
• Gender → 1 = Male, 2 = Female

👉 Download Dataset

Step-by-Step Cox Regression in MedCalc

Step 1: Open Analysis

Go to:
👉 Statistics → Survival Analysis → Cox regression

Step 2: Select Variables

• Survival time → Time_months_
• Endpoint → Status
  • Define: 1 = Event, 0 = Censored
• Predictor variables →
  • Treatment
  • Age
  • Gender

Step 3: Choose Method

Enter (Used in your analysis)

• Includes all variables simultaneously
• Best for beginners

Step 4: Categorical Variables

👉 Click Categorical…
Select:

• Treatment
• Gender

Step 5: Graph Options

• ✔ Survival probability (%)
• ✔ Graph subgroups (optional)

Step 6: Run Analysis

Click OK

Options Explanation

Method Options

👉 Your analysis used Enter method ✅

P-value Settings

• Enter variable if P < 0.05
• Remove if P > 0.1

👉 Used only in stepwise methods

Graph Options

• Survival probability (%) → Standard curve
• 100 – survival → Failure probability

Results Interpretation

Case Summary

• Total cases = 30
• Events = 18 (60%)
• Censored = 12 (40%)

👉 Dataset is sufficient for Cox regression

Model Fit

• Chi-square = 2.090
• P-value = 0.554

👉 ❗ Model is not statistically significant

Hazard Ratios (Important)

Treatment

• HR = 2.57
  👉 Patients in Drug B group may have higher risk
  👉 But P = 0.282 → Not significant

Age

• HR ≈ 1
  👉 No effect on survival

Gender

• HR = 3.64
  👉 Higher risk in one gender
  👉 But not significant

Survival Curve Interpretation

Based on your graph:

• Survival starts at 100%
• Gradually decreases over time
• Step-like pattern shows event occurrence

Key Observations

✔ Sharp drops = events
✔ Flat line = no events
✔ End survival ≈ 10–15%

👉 Indicates decreasing survival probability over time

Baseline Hazard Interpretation

• Survival reduces from 97% → 11%
• Shows increasing cumulative hazard

Model Accuracy

• Harrell’s C-index = 0.590

👉 Interpretation:

• Model has moderate predictive ability

Important Note

Even though hazard ratios are high:
👉 P-values are not significant

👉 This means:

• No strong statistical evidence
• Larger sample size needed

Conclusion

Cox Proportional Hazards Regression is a powerful tool for analyzing survival data and identifying risk factors in biomedical studies. In this MedCalc tutorial:

✔ We learned how to prepare numeric data
✔ Performed Cox regression step-by-step
✔ Explained options and methods
✔ Interpreted hazard ratios and survival curve

Although the model was not statistically significant, the analysis demonstrates how Cox regression helps evaluate survival risk.