If you work with time-to-event data (clinical outcomes, reliability, churn/retention, follow-up studies), you already know the pain: survival analysis is powerful, but many workflows pull you out of Excel and into a separate stats stack.
BESHStatNG brings survival analysis directly into Excel—so you can prepare data, run analyses, and generate publication-ready outputs where your team already works.
Kaplan–Meier Plot: Survival Curves You Can Share
The Kaplan–Meier module in BESHStatNG creates classic step-function survival curves in an Excel worksheet, with options that make the chart immediately usable for reporting and collaboration:
- Stepwise survival curves per group
- Censoring markers placed at the appropriate survival probability
- Optional 95% confidence intervals
- Optional legend and chart title
- Time-axis labeling (e.g., days, months) for clearer interpretation
Because the output is a native Excel chart, you can fine-tune formatting, copy directly into slides, and keep results alongside your source data.
Read the Kaplan–Meier Plot documentation →
Log-rank Test: Compare Survival Curves (More Than One Way)
When you need to test whether survival curves differ between groups, BESHStatNG includes a weighted log-rank framework with multiple commonly used weighting schemes, including:
- Log-rank (equal weights)
- Gehan–Breslow
- Tarone–Ware
- Peto–Peto
- Modified Peto (Andersen)
For the common two-group case, the output also includes a hazard ratio with an approximate 95% confidence interval, making it easier to communicate effect size, not just significance.
Read the Log-rank Test documentation →
Cox Regression: Proportional Hazards Modeling Inside Excel
When you want to adjust for covariates or build a multivariable survival model, BESHStatNG provides Cox proportional hazards regression as a first-class feature in the add-in.
Under the hood, the implementation supports practical, real-world modeling needs:
- Newton–Raphson fitting with step-halving for stable convergence
- Tie handling (Breslow and Efron methods)
- Model-based covariance and robust (sandwich) covariance options
- Stratification support for stratified Cox models
- Diagnostics and residuals (including Schoenfeld / scaled Schoenfeld, martingale, deviance, dfbeta/dfbetas, and more) to help validate assumptions and identify influential observations
This means you can fit, interpret, and validate Cox models without breaking your Excel workflow—ideal for teams that need transparency and reproducibility in spreadsheets.
Read the Cox Regression documentation →
What You Get: A Practical Survival Analysis Workflow
With BESHStatNG, a typical workflow stays simple:
- Prepare your time-to-event dataset in Excel (time, event indicator, grouping/covariates)
- Generate Kaplan–Meier curves with censoring and optional confidence limits
- Use log-rank tests (and weighted variants) to compare groups
- Fit Cox regression for adjusted effects, stratification, and deeper diagnostics
Try It on Your Data
If you already have survival data in Excel, BESHStatNG is designed to help you move from dataset to interpretable results quickly—without switching tools.
Start here:
Kaplan–Meier Plot |
Log-rank Test |
Cox Regression
Questions or feature requests? I’m actively improving BESHStatNG—feedback from real-world analyses helps shape what comes next.