The Use of Real-World Data for Studies of Dynamic Disease Processes
Bibliographic record
Abstract
Obtaining valid real-world evidence about intervention effects from observational cohorts or administrative health records data is challenging. Visits to healthcare providers tend to occur more often during periods of increased disease activity and symptom exacerbation, or upon disease progression. Treatments likewise tend to change when it is apparent that disease activity has increased or a meaningful progression has occurred. This creates a dual problem in which patient visits are disease-related and treatments changes are driven by disease condition and clinical presentation. Disease-related visits and treatment by indication can produce a biased impression of the disease process in the target population and of the effects of treatment. We discuss how these challenges can be addressed through the use of joint models for the disease, marker, and treatment processes, as well as the observation (visit) process. Using illustrative multistate models, we demonstrate the biases that can arise from various types of analyses and show how estimators from fitting such joint models to persons with psoriatic arthritis can be used to gain scientific insights and address common questions about treatment effects.
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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 | 0.099 | 0.412 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".