search for


Review Article
What Should We Consider Carefully When Performing Survival Analysis?
Clin Pediatr Hematol Oncol 2019;26:1-5.
Published online April 30, 2019
© 2019 Korean Society of Pediatric Hematology-Oncology and Korean Society for Pediatric Neuro-Oncology

Sang Gyu Kwak1, and Eun Jin Choi2

Departments of 1Medical Statistics and 2Pediatrics, School of Medicine, Catholic University of Daegu, Daegu, Korea
Correspondence to: Eun Jin Choi
Department of Pediatrics, School of Medicine, Catholic University of Daegu, 33 Duryugongwon-ro 17-gil, Nam-gu, Daegu 42472, Korea
Tel: +82-53-650-4248
Fax: +82-53-621-4106
Received March 9, 2019; Revised March 26, 2019; Accepted April 3, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
The survival data and the survival analysis are the data and analysis methods used to study the probability of survival. The survival data consist of a period from the juncture of a start event to the juncture of the end event (occurrence event). The period is called the survival period or survival time. In this way, the method of analysing the survival time of subjects and appropriately summarizing the degree of survival is called survival analysis. To understand and analyse survival analysis methods, researchers must be aware of some concepts. Concepts to be aware of in the survival analysis include events, censored data, survival period, survival function, survival curve and so on. This review focuses on the terms and concepts used in the survival analysis. It will also cover the types of survival data that should be collected and prepared when using actual survival analysis method and how to prepare them.
Keywords: Censored data, Hazard ratio, Kaplan-Meier method, Survival analysis
  1. Park Y, Lim J, Kim S, et al. The prognostic impact of lymphocyte subsets in newly diagnosed acute myeloid leukemia. Blood Res 2018;53:198-204.
    Pubmed KoreaMed CrossRef
  2. Kang J, Yoon S, Suh C. Relevance of prognostic index with β2-microglobulin for patients with diffuse large B-cell lymphoma in the rituximab era. Blood Res 2017;52:276-84.
    Pubmed KoreaMed CrossRef
  3. Jung SH, Ahn SY, Choi HW, et al. STAT3 expression is associated with poor survival in non-elderly adult patients with newly diagnosed multiple myeloma. Blood Res 2017;52:293-9.
    Pubmed KoreaMed CrossRef
  4. Mouracade P. Key concepts of survival analysis: Checking appropriateness. Prog Urol 2017;27:331-3.
    Pubmed CrossRef
  5. Canales RA, Wilson AM, Pearce-Walker JI, Verhougstraete MP, Reynolds KA. Methods for handling left-censored data in quantitative microbial risk assessment. Appl Environ Microbiol 2018;84.
    Pubmed KoreaMed CrossRef
  6. George B, Seals S, Aban I. Survival analysis and regression models. J Nucl Cardiol 2014;21:686-94.
    Pubmed KoreaMed CrossRef
  7. Delgado J, Pereira A, Villamor N, López-Guillermo A, Rozman C. Survival analysis in hematologic malignancies: recommendations for clinicians. Haematologica 2014;99:1410-20.
    Pubmed KoreaMed CrossRef

October 2019, 26 (2)
Full Text PDF
Send to a friend

Cited By Articles
  • CrossRef (0)

Author ORCID Information
  • Eun Jin Choi