Literature DB >> 32656940

Cancer survival statistics for patients and healthcare professionals - a tutorial of real-world data analysis.

S Eloranta1, K E Smedby1,2, P W Dickman3, T M Andersson3.   

Abstract

Monitoring survival of cancer patients using data collected by population-based cancer registries is an important component of cancer control. In this setting, patient survival is often summarized using net survival, that is survival from cancer if there were no other possible causes of death. Although net survival is the gold standard for comparing survival between groups or over time, it is less relevant for understanding the anticipated real-world prognosis of patients. In this review, we explain statistical concepts targeted towards patients, clinicians and healthcare professionals that summarize cancer patient survival under the assumption that other causes of death exist. Specifically, we explain the appropriate use, interpretation and assumptions behind statistical methods for competing risks, loss in life expectancy due to cancer and conditional survival. These concepts are relevant when producing statistics for risk communication between physicians and patients, planning for use of healthcare resources, or other applications when consideration of both cancer outcomes and the competing risks of death is required. To reinforce the concepts, we use Swedish population-based data of patients diagnosed with cancer of the breast, prostate, colon and chronic myeloid leukaemia. We conclude that when choosing between summary measures of survival it is critical to characterize the purpose of the study and to determine the nature of the hypothesis under investigation. The choice of terminology and style of reporting should be carefully adapted to the target audience and may range from summaries for specialist readers of scientific publications to interactive online tools aimed towards lay persons.
© 2020 The Authors. Journal of Internal Medicine published by John Wiley & Sons Ltd on behalf of Association for Publication of The Journal of Internal Medicine.

Entities:  

Keywords:  biostatistics; cancer; death risk; epidemiology

Mesh:

Year:  2020        PMID: 32656940     DOI: 10.1111/joim.13139

Source DB:  PubMed          Journal:  J Intern Med        ISSN: 0954-6820            Impact factor:   8.989


  16 in total

1.  Reference-Adjusted Loss in Life Expectancy for Population-Based Cancer Patient Survival Comparisons-with an Application to Colon Cancer in Sweden.

Authors:  Therese M-L Andersson; Mark J Rutherford; Bjørn Møller; Paul C Lambert; Tor Åge Myklebust
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2022-09-02       Impact factor: 4.090

2.  Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study.

Authors:  Liwei Wei; Yongdi Huang; Zheng Chen; Hongyu Lei; Xiaoping Qin; Lihong Cui; Yumin Zhuo
Journal:  Front Oncol       Date:  2021-10-14       Impact factor: 6.244

3.  ANXA2P2: A Potential Immunological and Prognostic Signature in Ovarian Serous Cystadenocarcinoma via Pan-Carcinoma Synthesis.

Authors:  Yanna Zhang; Ting Du; Xiancheng Chen
Journal:  Front Oncol       Date:  2022-02-08       Impact factor: 6.244

4.  Assessing lead time bias due to mammography screening on estimates of loss in life expectancy.

Authors:  Elisavet Syriopoulou; Alessandro Gasparini; Keith Humphreys; Therese M-L Andersson
Journal:  Breast Cancer Res       Date:  2022-02-23       Impact factor: 6.466

5.  LncRNA ASMTL-AS1/microRNA-1270 differentiate prognostic groups in gastric cancer and influence cell proliferation, migration and invasion.

Authors:  Zhenhe Song; Jian Wang
Journal:  Bioengineered       Date:  2022-01       Impact factor: 3.269

6.  Development and Validation of Genome Instability-Associated lncRNAs to Predict Prognosis and Immunotherapy of Patients With Hepatocellular Carcinoma.

Authors:  Yifeng Yan; Liang Ren; Yan Liu; Liang Liu
Journal:  Front Genet       Date:  2022-01-28       Impact factor: 4.599

Review 7.  Application of Proteomics in the Discovery of Radiosensitive Cancer Biomarkers.

Authors:  Hui Luo; Hong Ge
Journal:  Front Oncol       Date:  2022-02-23       Impact factor: 6.244

8.  LncRNA PCAT1 activates SOX2 and suppresses radioimmune responses via regulating cGAS/STING signalling in non-small cell lung cancer.

Authors:  Yanping Gao; Nannan Zhang; Zihang Zeng; Qiuji Wu; Xueping Jiang; Shuying Li; Wenjie Sun; Jianguo Zhang; Yangyi Li; Jiali Li; Fajian He; Zhengrong Huang; Jinfang Zhang; Yan Gong; Conghua Xie
Journal:  Clin Transl Med       Date:  2022-04

9.  Cancer outcomes research-a European challenge: measures of the cancer burden.

Authors:  Mette Kalager; Hans-Olov Adami; Pernilla Lagergren; Karen Steindorf; Paul W Dickman
Journal:  Mol Oncol       Date:  2021-06-22       Impact factor: 6.603

10.  Relative and absolute cancer risks among Nordic kidney transplant recipients-a population-based study.

Authors:  Henrik Benoni; Sandra Eloranta; Dag O Dahle; My H S Svensson; Arno Nordin; Jan Carstens; Geir Mjøen; Ilkka Helanterä; Vivan Hellström; Gunilla Enblad; Eero Pukkala; Søren S Sørensen; Marko Lempinen; Karin E Smedby
Journal:  Transpl Int       Date:  2020-09-25       Impact factor: 3.842

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