Integrating Data From Randomized Controlled Trials and Observational Studies to Assess Survival in Rare Diseases
Bibliographic record
Abstract
Background Conducting randomized controlled trials to investigate survival in a rare disease like pulmonary arterial hypertension has considerable ethical and logistical constraints. In many studies, such as the Study with an Endothelin Receptor Antagonist in Pulmonary Arterial Hypertension to Improve Clinical Outcome (SERAPHIN) randomized controlled trial, evaluating survival is further complicated by bias introduced by allowing active therapy among placebo-treated patients who clinically deteriorate. Methods and Results SERAPHIN enrolled and followed patients in the same time frame as the US Registry to Evaluate Early And Long-term PAH Disease Management, providing an opportunity to compare observed survival for SERAPHIN patients with predicted survival had they received real-world treatment as in the Registry to Evaluate Early And Long-term PAH Disease Management. From the Registry to Evaluate Early And Long-term PAH Disease Management (N=3515), 734 patients who met SERAPHIN eligibility criteria were selected and their data used to build a prediction model for time to death up to 3 years based on 10 baseline prognostic variables. The model was used to predict a survival curve for each of the 742 SERAPHIN patients via their baseline variables. The average of these predicted survival curves was compared with observed survival of the placebo (n=250) and macitentan 10 mg (n=242) groups using a log-rank test and Cox proportional hazard model. Observed mortality risk for patients randomized to placebo, 62% of whom were taking background pulmonary arterial hypertension therapy, tended to be lower than that predicted for all SERAPHIN patients (16% lower; P=0.259). The observed placebo survival curve closely approximated the predicted survival curve for the first 15 months. Beyond that time, observed risk of mortality decreased compared with predicted mortality, potentially reflecting the impact of crossover of patients in the placebo group to active therapy. Over 3 years, risk of mortality observed with macitentan 10 mg was 35% lower than predicted mortality ( P=0.010). Conclusions These analyses show that, in a rare disease, real-world observational data can complement randomized controlled trial data to overcome some challenges associated with assessing survival in the setting of a randomized controlled trial. Clinical Trial Registration https://www.clinicaltrials.gov . Unique identifiers: NCT00660179 and NCT00370214.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".