ArticleESMO real world data and digital oncology2026
Evaluating the statistical utility and information loss in the transformation of a real-world oncology database from CDISC-SDTM to OMOP-CDM.
Article in ESMO real world data and digital oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Bridging Gaps in Standards for the Secondary Use of Health Data: Exploratory Standard, Tool Assessment, and Feasibility Study.JMIR formative research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This project documented the conversion of a French real-world oncology study database from Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The goal was to measure syntactic and semantic information loss resulting from this transformation. Patients and methods: Source data originated from a retrospective observational study of HER2-positive early breast cancer patients. Initially in CDISC-SDTM standards, the data included 73 variables detailing patient demographics, disease characteristics, surgery, and follow-up information such as adjuvant treatment. Data conversion to OMOP-CDM utilized extract, load and transform (ELT) procedures with Observational Health Data Sciences and Informatics (OHDSI) tools and the data build tool (dbt), encountering challenges in mapping specific variables and maintaining data granularity. Information loss assessments involved statistical analyses. Results: The source database was successfully mapped to OMOP-CDM and standardized terminologies. Statistical results from the OMOP-transformed database were consistent with those from the original SDTM database, achieving 100% concordance across all tested equality criteria for univariate, bivariate, logistic, survival, and correlation analyses at a 95% confidence interval or respective Conclusions: Statistical utility of the real-world oncology dataset was maintained after transformation to OMOP-CDM, ensuring reproducibility of statistical analyses. Information loss during conversion is significantly dependent on the intrinsic characteristics and level of standardization of the source database, particularly if not originally adhering to standard vocabularies.
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.