Published on 22/12/2025
Top Software Tools for Performing Time-to-Event Analyses in Clinical Trials
Time-to-event analysis, often referred to as survival analysis, is a fundamental statistical method in clinical trials, especially in fields like oncology, cardiology, and infectious disease. Whether assessing Overall Survival (OS), Progression-Free Survival (PFS), or Time to Progression (TTP), professionals rely on robust software tools to conduct accurate and reproducible analyses. Choosing the right software is essential for regulatory compliance, reproducibility, and efficient trial decision-making.
This guide outlines the leading software platforms used for time-to-event analyses, including their strengths, typical applications, and how they support statistical requirements in clinical research. Each tool is widely used in pharma biostatistics teams and aligns with best practices encouraged by agencies such as the USFDA.
Why Software Selection Matters for Time-to-Event Analysis
Time-to-event data is unique due to censoring, variable follow-up times, and the need for visual survival curves. The right software can:
- Efficiently handle right-censored data
- Generate Kaplan-Meier (KM) plots
- Estimate hazard ratios via Cox proportional hazards models
- Conduct log-rank tests and stratified analyses
- Support interim survival analysis protocols
Proper software use also supports pharma validation requirements for reproducibility and data traceability.
1. R: The Open-Source Powerhouse
Best for: Flexibility, advanced modeling, reproducibility
- Key
survival, survminer, rms
R is widely adopted by statisticians and academic researchers and is ideal for data exploration, simulation, and regulatory reports. Use ggsurvplot() from survminer for polished survival visualizations, commonly seen in stability study reports and oncology submissions.
2. SAS: Industry Standard for Clinical Submissions
Best for: Regulatory trials, standardization, large datasets
- Procedures:
PROC LIFETEST,PROC PHREG - Built-in support for Kaplan-Meier, log-rank test, Cox models
- Preferred by CROs and large pharma for FDA/EMA submissions
- Compatible with CDISC/ADaM datasets
SAS is indispensable for compliant trial environments where outputs must meet strict formatting and validation standards. Many pharma SOPs mandate SAS for primary analysis datasets.
3. STATA: User-Friendly with Powerful Survival Tools
Best for: Quick model fitting, intuitive syntax
- Commands:
stset,sts,stcox - Easy handling of time-varying covariates
- Graphical KM curves with automatic risk tables
- Well-documented output with hazard ratios and p-values
STATA is often used in academic clinical research centers and hybrid data teams. Its blend of command-line power and GUI options makes it a favorite for biostatisticians at all levels.
4. Python (Lifelines Package): Ideal for Data Science Integration
Best for: Tech-savvy teams, automation, integration with machine learning
- Popular library:
lifelines - Functions:
KaplanMeierFitter,CoxPHFitter - Seamless with Pandas and NumPy dataframes
- Compatible with Jupyter Notebooks for exploratory survival modeling
Python is emerging in pharma analytics for reproducible pipelines, adaptive designs, and digital health trials. While not yet a regulatory mainstay, it’s excellent for innovation and secondary analyses.
5. SPSS: Accessible but Limited
Best for: Basic analyses, educational environments
- Menus for Kaplan-Meier, log-rank, Cox regression
- No coding required—ideal for non-programming users
- Limited flexibility in advanced modeling or scripting
SPSS is suitable for introductory trial design teams or those focused on smaller datasets. It is not generally used in large-scale or regulatory-facing studies.
Software Feature Comparison
| Feature | R | SAS | STATA | Python | SPSS |
|---|---|---|---|---|---|
| KM Curve | ✓ | ✓ | ✓ | ✓ | ✓ |
| Cox Model | ✓ | ✓ | ✓ | ✓ | ✓ |
| Graph Customization | High | Medium | High | High | Low |
| Regulatory Use | Yes | Yes | Yes | No (limited) | No |
| Scripting Support | ✓ | ✓ | ✓ | ✓ | ✗ |
Best Practices When Using Software for Survival Analysis
- Always predefine your analysis plan in the SAP
- Use validated software as per company policy
- Check proportional hazards assumption in Cox models
- Include censoring indicators in Kaplan-Meier plots
- Document version numbers and output formats for audit purposes
- Link outputs with Stability Studies or trial documentation systems
Regulatory Guidance on Software Use
While no specific software is mandated, regulatory bodies expect transparency, reproducibility, and traceability. Adherence to ICH E9 and ICH E3 standards is critical when submitting survival analyses, especially in adaptive or event-driven designs.
In practice, R and SAS remain the most accepted for formal submissions, while STATA and Python complement exploratory work. Always pair your software with compliant GMP documentation.
Conclusion: Match the Tool to Your Trial’s Needs
Time-to-event analyses are integral to modern clinical trials, and selecting the right software ensures robust, defensible results. From R’s open-source flexibility to SAS’s regulatory muscle and Python’s modern workflows, there’s a tool for every stage of survival analysis. Understand your protocol, regulatory goals, and team’s technical capability to make the optimal choice.