Observational studyThe Lancet. Oncology2024
Validity and timeliness of cancer diagnosis data collected during a prospective cohort study and reported by the English and Welsh cancer registries: a retrospective, comparative analysis.
Observational study in The Lancet. Oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Diagnostic Implications of Multi-Cancer Early Detection Testing in the Investigation of Cancer Symptoms: An Exploratory Retrospective Analysis of the SYMPLIFY Study.The Lancet regional health. Europe · 2026Article
- Associations between respiratory diseases and lung cancer risk: a secondary analysis of the large-scale prospective UK biobank cohort.BMJ open respiratory research · 2026Article
- Multiple imputation for missing values in ordinal variables from cancer registry data when performing Cox proportional hazards regression.BMC medical research methodology · 2026Article
- The Danish Drowning Cohort: Evaluation of Data Availability for Fatal and Non-Fatal Drowning Incidents.Clinical epidemiology · 2026Article
- Letter to the Editor: Worldwide research on 3D printing for cancer: a dual-method analysis of bibliometrics and stratified focused thematic.International journal of surgery (London, England) · 2026Article
- Utilisation patterns of systemic anticancer therapy (SACT) in patients with advanced non-small cell lung cancer (NSCLC) in England.BMJ oncology · 2026Article
- CA125 and age-based models for ovarian cancer detection in primary care: a population-based external validation study.British journal of cancer · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCancer places a high burden on society and health-care systems. Cancer research requires high-quality data, which is resource-intensive to obtain. Using administrative datasets such as cancer registries could improve the efficiency of cancer studies if data were valid and timely. We aimed to compare the validity and timeliness of diagnostic cancer data on-site during the SYMPLIFY study to that obtained from the cancer registries of England and Wales.
methodsCancer data were collected from 5461 participants across 44 hospital sites during a prospective observational study in England and Wales, SYMPLIFY (ISRCTN10226380). Linked cancer data were obtained from Digital Health and Care Wales (DHCW), the Welsh Cancer Intelligence and Surveillance Unit (WCISU), and the English National Cancer Registration Dataset (NCRD) and Rapid Cancer Registration Dataset (RCRD), regularly between April, 2022, and September, 2023. The primary objectives of the study were to evaluate the validity (via assessment of the proportion of completed data fields and concordance with SYMPLIFY sites), and timeliness of the data in all datasets, for all cancers diagnosed within 9 months of study enrolment. Data fields investigated were cancer site via International Classification of Disease, 10th Revision (ICD-10) code; cancer morphology via International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) morphology histology code and broad morphological grouping; overall stage; and TNM classification.
findingsFor data collected between April, 2022, and September, 2023, completeness at the last data cut available for each dataset ranged from 84% to 100% for ICD-O-3 morphology, from 43% to 100% for overall stage, and from 74% to 83% for TNM stage. The concordance between SYMPLIFY data and NCRD was 96% (95% CI 92-98) for ICD-10, 60% (53-66) for ICD-O-3 morphology, 83% (78-88) for ICD-O-3 broad morphology groupings, 73% (67-78) for stage, and 51% (44-59) for TNM; and with WCISU was 89% (95% CI 81-94) for ICD-10, 63% (53-73) for ICD-O-3 morphology, 80% (70-87) for ICD-O-3 broad morphology groupings, 83% (74-90) for overall stage, and 49% (38-61) for TNM stage. Concordance between SYMPLIFY and RCRD was 95% (95% CI 92-98) for ICD-10, 67% (60-74) for ICD-O-3 morphology, 85% (79-90) for ICD-O-3 broad morphology groupings, and 73% (65-80) for overall stage; and between SYMPLIFY and DHCW was 96% (91-99) for ICD-10, 74% (64-83) for ICD-O-3 morphology, 84% (75-91) for ICD-O-3 broad morphology groupings, and 87% (74-95) for stage. The SYMPLIFY dataset reached completion at 12 months post-enrolment in November, 2022, compared with 13 months for NCRD in December, 2023. RCRD and DHCW reached completion at 13 months and 15 months post-enrolment, in December, 2022, and February, 2023, respectively.
interpretationWe report similar completeness of data fields, concordance, and timeliness between on-site and centrally collected cancer outcomes data. Our findings suggest that central registry data can help alleviate the resource burden in clinical trials and improve cancer research. Cancer registries might need additional resources to provide data for registry-based trials at scale.
fundingGRAIL Bio UK.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.