Survival analysis software for clinical research Kaplan-Meier survival curves with four group comparison tests, Cox proportional hazards modelling with hazard ratios and likelihood-ratio tests, and restricted mean survival time — with results that go straight into a manuscript.
Try free for 15 days — no credit card required
Trusted by 75,000 researchers, analysts, and scientists at leading universities, hospitals, and companies worldwide — for over 30 years.
Kaplan-Meier with four comparison tests
Log-rank, Wilcoxon, Tarone-Ware, and Fleming-Harrington tests for comparing survival between groups. Pointwise, Nair, and Hall-Wellner confidence bands. Median and quartile survival times with confidence intervals.
Cox proportional hazards
Model the effect of covariates on survival. Hazard ratios with Wald confidence intervals, likelihood-ratio tests for each term, and baseline survival function estimation. Log-log plots to assess the proportional hazards assumption.
Patient data stays local
Analyse-it runs entirely within Excel on your PC. No cloud processing, no data transmission — your patient data stays under institutional data governance and ethics approval controls.
Most survival analysis tools either require programming , cost more than clinical researchers can justify , or sit in a standalone application with a proprietary file format that makes sharing results with co-authors and supervisors inconvenient. For researchers whose data is already in Excel and whose output goes into a manuscript, the overhead of switching to a separate environment for time-to-event analysis is real — importing data, learning an interface, exporting results back, and managing yet another file format.
Analyse-it provides Kaplan-Meier estimation and Cox proportional hazards modelling inside Excel with the same depth as dedicated packages. Kaplan-Meier survival curves with four group comparison tests — log-rank, Wilcoxon, Tarone-Ware, and Fleming-Harrington — each weighting different parts of the survival curve, so you can choose the test appropriate for your hypothesis. Three confidence band methods: pointwise, Nair (EP), and Hall-Wellner. Median and quartile survival times with confidence intervals. Restricted mean survival time for settings where median survival is not reached. Cox proportional hazards with hazard ratios, Wald confidence intervals, likelihood-ratio tests, and baseline survival function estimation. Log-log transformed survival plots to assess whether the proportional hazards assumption holds before committing to the model.
Survival analysis is typically one part of a larger clinical study. The same dataset may need logistic regression for a prediction model, ROC curves for diagnostic accuracy, Bland-Altman for method agreement, or reference intervals for population norms. Analyse-it covers all of these in the Medical edition, so the analysis stays in one workbook rather than being spread across multiple tools. Results are defensible at peer review — every calculation runs in Analyse-it's own validated engine, not in Excel formulas.
75,000 researchers, analysts, and scientists at leading universities, hospitals, and research institutions have relied on Analyse-it for over 30 years.
Seen in the field
What's included
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Estimate survival and compare groups with Kaplan-Meier
Kaplan-Meier survival function with pointwise, Nair, and Hall-Wellner confidence bands. Compare groups using log-rank, Wilcoxon, Tarone-Ware, and Fleming-Harrington tests. Each test weights different parts of the survival curve — choose the one that matches your hypothesis about where the survival difference is expected.
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Summarise survival times and probabilities
Median and quartile survival times with confidence intervals. Survival probability at any specified time point. Restricted mean survival time for studies where median survival is not reached or where the mean is the more clinically relevant summary.
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Model the effect of covariates with Cox regression
Cox proportional hazards model with hazard ratios, Wald confidence intervals, and likelihood-ratio tests for each term. Baseline survival function estimation. Continuous and categorical covariates. Log-log plots to verify the proportional hazards assumption before interpreting the model.
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Assess model assumptions with transformed plots
Log and log-log transformed survival plots to check the proportional hazards assumption visually. If the assumption doesn't hold, the plots show it — before you commit to a model that reviewers will challenge.
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Defensible at peer review
Every calculation runs in Analyse-it's own validated engine — no Excel formulas or third-party functions. The numbers in a manuscript can be defended under reviewer scrutiny. See the development and validation process.
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Part of the Medical edition
Survival analysis is part of the Medical edition, which also covers ROC curves and diagnostic accuracy, logistic regression, Bland-Altman agreement analysis, reference intervals, and the full standard statistical toolkit — ANOVA, regression, hypothesis testing, and non-parametric tests. Complete clinical statistics in one package.
Trusted by 75,000 researchers, analysts, and scientists at leading universities, hospitals, and companies worldwide — for over 30 years.
Try free for 15 days — no credit card required
