Survival Analyses: A Statistical Review for Surgeons

Published:January 26, 2022DOI:
      Survival analyses are a group of statistical principles applied to accurately analyze the length of time until a previously defined event occurs. Increasing survival is the underlying goal for most medical interventions, and is particularly critical in oncology-related surgical fields. In order to justify the application of a novel surgical intervention or other cancer therapy, one must first definitively show improvements in patient survival compared to the existing method of treatment. In order to definitively recommend one treatment option over another, it is paramount to design a study that addresses and minimizes sources of bias where possible. This can be challenging due to numerous factors including selecting an appropriate study design, dealing with censored data, obtaining an appropriate sample size, and performing robust statistical analysis. It is critical for surgeons in every stage of training to be able to both understand and apply these methods in order to improve patient care. In this review, we discuss approaches to design survival studies, relevant errors/biases, and how to account for them and cover several field-standard methods to analyze survival data including Kaplan-Meier plots, the log-rank test, and Cox Proportional Hazards Models. Importantly, this is accompanied by easily repurposable examples of how to implement these analyses in both R and GraphPad Prism using a publicly available survival dataset. This review will assist surgeons of all training levels in the design and analysis of survival studies and serve as a starting point for advancing patient care.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Seminars in Thoracic and Cardiovascular Surgery
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


      1. The R Project for Statistical Computing. [cited 19 Sep 2021]. Available:

      2. Home - GraphPad. [cited 19 Sep 2021]. Available:

        • Wu M.
        • Tsuboi Y.-L.
        • He J.
        • et al.
        Osimertinib in Resected -Mutated Non-Small-Cell Lung Cancer.
        N Engl J Med. 2020; 383: 1711-1723
        • Stokes W.A.
        • Bronsert M.R.
        • Meguid R.A.
        • et al.
        Post-treatment mortality after surgery and stereotactic body radiotherapy for early-stage non-small-cell lung cancer.
        J Clin Oncol. 2018; 36: 642-651
        • Bland J.M.
        • Altman D.G.
        Survival probabilities (the Kaplan-Meier method).
        BMJ. 1998; 317: 1572
        • Suresh K.
        • Chandrashekara S
        Sample size estimation and power analysis for clinical research studies.
        J Hum Reprod Sci. 2012; 5: 7-13
        • Rich J.T.
        • Neely J.G.
        • Paniello R.C.
        • et al.
        A practical guide to understanding Kaplan-Meier curves.
        Otolaryngol Head Neck Surg. 2010; 143: 331-336
        • Bland J.M.
        • Altman D.G.
        The log-rank test.
        BMJ. 2004; 328: 1073
        • Cox D.R.
        Regression models and life-tables.
        J Royal Stats Soc Series B (Methodological). 1972; 34: 187-202
        • Austin P.C.
        • Lee D.S.
        • Fine J.P
        Introduction to the analysis of survival data in the presence of competing risks.
        Circulation. 2016; 133: 601-609
        • Barraclough H.
        • Simms L.
        • Govindan R.
        Biostatistics primer: what a clinician ought to know: hazard ratios.
        J Thorac Oncol. 2011; 6: 978-982
        • Spruance S.L.
        • Reid J.E.
        • Grace M.
        • et al.
        Hazard ratio in clinical trials.
        Antimicrob Agents Chemother. 2004; 48
        • Noble W.S.
        How does multiple testing correction work?.
        Nat Biotechnol. 2009; 27: 1135-1137