Florence Bourgeois

Qixuan Hu, Xumou Zhang, Jinman Kim et al.

Objectives: With accurate estimates of expected safety results, clinical trials could be better designed and monitored. We evaluated methods for predicting serious adverse event (SAE) results in clinical trials using information only from their registrations prior to the trial. Material and Methods: We analyzed 22,107 two-arm parallel interventional clinical trials from ClinicalTrials.gov with structured summary results. Two prediction models were developed: a classifier predicting whether a greater proportion of participants in an experimental arm would have SAEs (area under the receiver operating characteristic curve; AUC) compared to the control arm, and a regression model to predict the proportion of participants with SAEs in the control arms (root mean squared error; RMSE). A transfer learning approach using pretrained language models (e.g., ClinicalT5, BioBERT) was used for feature extraction, combined with a downstream model for prediction. To maintain semantic representation in long trial texts exceeding localized language model input limits, a sliding window method was developed for embedding extraction. Results: The best model (ClinicalT5+Transformer+MLP) had 77.6% AUC when predicting which trial arm had a higher proportion of SAEs. When predicting SAE proportion in the control arm, the same model achieved RMSE of 18.6%. The sliding window approach consistently outperformed direct comparisons. Across 12 classifiers, the average absolute AUC increase was 2.00%, and absolute RMSE reduction was 1.58% across 12 regressors. Discussion: Summary results data from ClinicalTrials.gov remains underutilized. Predicted results of publicly reported trials provides an opportunity to identify discrepancies between expected and reported safety results.

Adam G. Dunn, Enrico Coiera, Florence Bourgeois

Objectives: Trial registries can be used to measure reporting biases and support systematic reviews but 45% of registrations do not provide a link to the article reporting on the trial. We evaluated the use of document similarity methods to identify unreported links between ClinicalTrials.gov and PubMed. Study Design and Setting: We extracted terms and concepts from a dataset of 72,469 ClinicalTrials.gov registrations and 276,307 PubMed articles, and tested methods for ranking articles across 16,005 reported links and 90 manually-identified unreported links. Performance was measured by the median rank of matching articles, and the proportion of unreported links that could be found by screening ranked candidate articles in order. Results: The best performing concept-based representation produced a median rank of 3 (IQR 1-21) for reported links and 3 (IQR 1-19) for the manually-identified unreported links, and term-based representations produced a median rank of 2 (1-20) for reported links and 2 (IQR 1-12) in unreported links. The matching article was ranked first for 40% of registrations, and screening 50 candidate articles per registration identified 86% of the unreported links. Conclusions: Leveraging the growth in the corpus of reported links between ClinicalTrials.gov and PubMed, we found that document similarity methods can assist in the identification of unreported links between trial registrations and corresponding articles.