Evidence map›Paper›PMID 30700859›Full record

ReviewNature reviews. Clinical oncology2019

Real-world data: towards achieving the achievable in cancer care.

Christopher M Booth, Safiya Karim, William J Mackillop

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Clinical oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 201 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
201citing papers in PubMed, 3 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

201 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Survivorship care for people affected by advanced or metastatic cancer: MASCC-ASCO standards and practice recommendations.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024
    Pooled it
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  4. Trial
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  11. Review
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  20. Observational

141 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Christopher M BoothDivision of Cancer Care and Epidemiology, Queen's University Cancer Research Institute, Kingston, Ontario, Canada. booth@queensu.ca.
Safiya KarimDepartment of Oncology, University of Calgary, Calgary, Alberta, Canada.
William J MackillopDivision of Cancer Care and Epidemiology, Queen's University Cancer Research Institute, Kingston, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of data from the real world to address clinical and policy-relevant questions that cannot be answered using data from clinical trials is garnering increased interest. Indeed, data from cancer registries and linked treatment records can provide unique insights into patients, treatments and outcomes in routine oncology practice. In this Review, we explore the quality of real-world data (RWD), provide a framework for the use of RWD and draw attention to the methodological pitfalls inherent to using RWD in studies of comparative effectiveness. Randomized controlled trials and RWD remain complementary forms of medical evidence; studies using RWD should not be used as substitutes for clinical trials. The comparison of outcomes between nonrandomized groups of patients who have received different treatments in routine practice remains problematic. Accordingly, comparative effectiveness studies need to be designed and interpreted very carefully. With due diligence, RWD can be used to identify and close gaps in health care, offering the potential for short-term improvement in health-care systems by enabling them to achieve the achievable.

Indexed as

Comparative Effectiveness ResearchElectronic Health RecordsHumansNeoplasmsRandomized Controlled Trials as TopicRegistries

Identifiers

What OpenQuestion holds

Textmetadata
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.