Evidence map›Paper›PMID 34694237›Full record

ArticleJMIR cancer2021

Exploring Cancer Survivor Needs and Preferences for Communicating Personalized Cancer Statistics From Registry Data: Qualitative Multimethod Study.

Ruben D Vromans, Mies C van Eenbergen, Gijs Geleijnse, Steffen Pauws, Lonneke V van de Poll-Franse, Emiel J Krahmer

Abstract read
In one paragraph

Article in JMIR cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–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

13 citing papers in PubMed.

  1. Trial
  2. Developing and Assessing the Acceptability of an Information Booklet for Patients in Surveillance for Abdominal Aortic Aneurysms: An Intervention Development Study.Health expectations : an international journal of public participation in health care and health policy · 2026
    Article
  3. Article
  4. Scoring Physician Risk Communication in Prostate Cancer Using Large Language Models.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026
    Article
  5. Scoring Physician Risk Communication in Prostate Cancer Using Large Language Models.medRxiv : the preprint server for health sciences · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Use of Persuasive Language in Communication of Risk during Prostate Cancer Treatment Consultations.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ruben D VromansDepartment of Communication and Cognition, Tilburg University, Tilburg, Netherlands.ORCID https://orcid.org/0000-0001-8040-1207
Mies C van EenbergenDepartment of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, Netherlands.ORCID https://orcid.org/0000-0001-6731-3296
Gijs GeleijnseDepartment of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, Netherlands.ORCID https://orcid.org/0000-0002-0947-8970
Steffen PauwsDepartment of Communication and Cognition, Tilburg University, Tilburg, Netherlands.ORCID https://orcid.org/0000-0003-2257-9239
Lonneke V van de Poll-FranseDepartment of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, Netherlands.ORCID https://orcid.org/0000-0003-0413-6872
Emiel J KrahmerDepartment of Communication and Cognition, Tilburg University, Tilburg, Netherlands.ORCID https://orcid.org/0000-0002-6304-7549

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDisclosure of cancer statistics (eg, survival or incidence rates) based on a representative group of patients can help increase cancer survivors' understanding of their own diagnostic and prognostic situation, and care planning. More recently, there has been an increasing interest in the use of cancer registry data for disclosing and communicating personalized cancer statistics (tailored toward personal and clinical characteristics) to cancer survivors and relatives.

objectiveThe aim of this study was to explore breast cancer (BCa) and prostate cancer (PCa) survivor needs and preferences for disclosing (what) and presenting (how) personalized statistics from a large Dutch population-based data set, the Netherlands Cancer Registry (NCR).

methodsTo elicit survivor needs and preferences for communicating personalized NCR statistics, we created different (non)interactive tools visualizing hypothetical scenarios and adopted a qualitative multimethod study design. We first conducted 2 focus groups (study 1; n=13) for collecting group data on BCa and PCa survivor needs and preferences, using noninteractive sketches of what a tool for communicating personalized statistics might look like. Based on these insights, we designed a revised interactive tool, which was used to further explore the needs and preferences of another group of cancer survivors during individual think-aloud observations and semistructured interviews (study 2; n=11). All sessions were audio-recorded, transcribed verbatim, analyzed using thematic (focus groups) and content analysis (think-aloud observations), and reported in compliance with qualitative research reporting criteria.

resultsIn both studies, cancer survivors expressed the need to receive personalized statistics from a representative source, with especially a need for survival and conditional survival rates (ie, survival rate for those who have already survived for a certain period). Personalized statistics adjusted toward personal and clinical factors were deemed more relevant and useful to know than generic or average-based statistics. Participants also needed support for correctly interpreting the personalized statistics and putting them into perspective, for instance by adding contextual or comparative information. Furthermore, while thinking aloud, participants experienced a mix of positive (sense of hope) and negative emotions (feelings of distress) while viewing the personalized survival data. Overall, participants preferred simplicity and conciseness, and the ability to tailor the type of visualization and amount of (detailed) statistical information.

conclusionsThe majority of our sample of cancer survivors wanted to receive personalized statistics from the NCR. Given the variation in patient needs and preferences for presenting personalized statistics, designers of similar information tools may consider potential tailoring strategies on multiple levels, as well as effective ways for providing supporting information to make sure that the personalized statistics are properly understood. This is encouraging for cancer registries to address this unmet need, but also for those who are developing or implementing personalized data-driven information tools for patients and relatives.

Indexed as

breast cancercancercancer registrycancer statisticspatient needs and preferencespersonalizationprostate cancerrisk communication

Identifiers

PMID34694237
PMCPMC8576563

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.