MétaCan
Menu
Back to cohort
Record W3155972762 · doi:10.1136/bmjopen-2020-043497

Can synthetic data be a proxy for real clinical trial data? A validation study

2021· article· en· W3155972762 on OpenAlexafffund
Zahra Azizi, Chaoyi Zheng, Lucy Mosquera, Louise Pilote, Khaled El Emam

Bibliographic record

VenueBMJ Open · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicData Quality and Management
Canadian institutionsUniversity of OttawaMcGill University Health CentreChildren's Hospital of Eastern OntarioMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsUnivariateMedicineBivariate analysisMultivariate statisticsMetric (unit)Synthetic dataStatisticMultivariate analysisData miningProxy (statistics)StatisticsInternal medicineComputer scienceMathematics

Abstract

fetched live from OpenAlex

OBJECTIVES: There are increasing requirements to make research data, especially clinical trial data, more broadly available for secondary analyses. However, data availability remains a challenge due to complex privacy requirements. This challenge can potentially be addressed using synthetic data. SETTING: Replication of a published stage III colon cancer trial secondary analysis using synthetic data generated by a machine learning method. PARTICIPANTS: There were 1543 patients in the control arm that were included in our analysis. PRIMARY AND SECONDARY OUTCOME MEASURES: Analyses from a study published on the real dataset were replicated on synthetic data to investigate the relationship between bowel obstruction and event-free survival. Information theoretic metrics were used to compare the univariate distributions between real and synthetic data. Percentage CI overlap was used to assess the similarity in the size of the bivariate relationships, and similarly for the multivariate Cox models derived from the two datasets. RESULTS: Analysis results were similar between the real and synthetic datasets. The univariate distributions were within 1% of difference on an information theoretic metric. All of the bivariate relationships had CI overlap on the tau statistic above 50%. The main conclusion from the published study, that lack of bowel obstruction has a strong impact on survival, was replicated directionally and the HR CI overlap between the real and synthetic data was 61% for overall survival (real data: HR 1.56, 95% CI 1.11 to 2.2; synthetic data: HR 2.03, 95% CI 1.44 to 2.87) and 86% for disease-free survival (real data: HR 1.51, 95% CI 1.18 to 1.95; synthetic data: HR 1.63, 95% CI 1.26 to 2.1). CONCLUSIONS: The high concordance between the analytical results and conclusions from synthetic and real data suggests that synthetic data can be used as a reasonable proxy for real clinical trial datasets. TRIAL REGISTRATION NUMBER: NCT00079274.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.424
metaresearch head score (Gemma)0.751
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.711

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4240.751
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0020.003
Science and technology studies0.0010.008
Scholarly communication0.0060.005
Open science0.0050.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.897
GPT teacher head0.691
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designBench or experimental
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations103
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueBMJ OpenSame topicData Quality and ManagementFrench-language works237,207