Evidence map›Paper›PMID 30655705›Full record

ArticleClinical epidemiology2019

Summarizing and communicating on survival data according to the audience: a tutorial on different measures illustrated with population-based cancer registry data.

Aurélien Belot, Aminata Ndiaye, Miguel-Angel Luque-Fernandez, Dimitra-Kleio Kipourou, Camille Maringe, Francisco Javier Rubio, Bernard Rachet

Erratum issuedAbstract read
In one paragraph

Article in Clinical epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Age at diagnosis, lifestyle factors, and risk of mortality: a city-wide cohort study of cancer survivors.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
    Article
  4. Monitoring Temporal Trends in Cancer Survival: Choosing Appropriate Standards When Accounting for Age and Other-Cause Mortality Variation Over Time.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Aurélien BelotCancer Survival Group, Department of Non-Communicable DiseaseEpidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK, aurelien.belot@lshtm.ac.uk.
Aminata NdiayeCancer Survival Group, Department of Non-Communicable DiseaseEpidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK, aurelien.belot@lshtm.ac.uk.
Miguel-Angel Luque-FernandezCancer Survival Group, Department of Non-Communicable DiseaseEpidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK, aurelien.belot@lshtm.ac.uk.
Dimitra-Kleio KipourouCancer Survival Group, Department of Non-Communicable DiseaseEpidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK, aurelien.belot@lshtm.ac.uk.
Camille MaringeCancer Survival Group, Department of Non-Communicable DiseaseEpidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK, aurelien.belot@lshtm.ac.uk.
Francisco Javier RubioCancer Survival Group, Department of Non-Communicable DiseaseEpidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK, aurelien.belot@lshtm.ac.uk.
Bernard RachetCancer Survival Group, Department of Non-Communicable DiseaseEpidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK, aurelien.belot@lshtm.ac.uk.

Funding

Cancer Research UK 18348Cancer Research UK 18525
6 · The paper itself

Abstract

Survival data analysis results are usually communicated through the overall survival probability. Alternative measures provide additional insights and may help in communicating the results to a wider audience. We describe these alternative measures in two data settings, the overall survival setting and the relative survival setting, the latter corresponding to the particular competing risk setting in which the cause of death is unavailable or unreliable. In the overall survival setting, we describe the overall survival probability, the conditional survival probability and the restricted mean survival time (restricted to a prespecified time window). In the relative survival setting, we describe the net survival probability, the conditional net survival probability, the restricted mean net survival time, the crude probability of death due to each cause and the number of life years lost due to each cause over a prespecified time window. These measures describe survival data either on a probability scale or on a timescale. The clinical or population health purpose of each measure is detailed, and their advantages and drawbacks are discussed. We then illustrate their use analyzing England population-based registry data of men 15-80 years old diagnosed with colon cancer in 2001-2003, aiming to describe the deprivation disparities in survival. We believe that both the provision of a detailed example of the interpretation of each measure and the software implementation will help in generalizing their use.

Indexed as

competing risksconditional survivalcrude probability of deathnet survivalnumber of life years lostrelative survival settingrestricted mean survival timesurvival

Identifiers

PMID30655705
PMCPMC6322561

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.