Clinical Trial Drug Safety Assessment With Interactive Visual Analytics
Bibliographic record
Abstract
In this article, we provide guidance on how statisticians can use interactive visual analytics to assist medical personnel through the entire clinical safety review process. For the general assessment of safety data (e.g., adverse events, laboratory measurements), we recommend a review flow with the first step using a display that leverages statistical methods to identify events with stronger evidence of a treatment difference. In combination with clinical knowledge, reviewers can identify events that need further scrutiny. Next, clinical reviewers will be provided with displays that show additional details on these events or patient level information to aid their decision making. For safety topics of interest (e.g., suicidal ideation and behavior), we propose a tailored approach to each topic both at the summary level as well as the patient level. Displays will show only information relevant to the specific topic and for the clinical questions of interest. We also discuss some challenges to fully implement and leverage interactive displays and tools. We encourage broad use of interactive visual analytics in the analysis and display of clinical data.Study sponsor: Eli Lilly and Company.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.204 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.084 | 0.019 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".