Sample Size Estimates for Clinical Trials of Vasospasm in Subarachnoid Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Clinical trials for prevention of vasospasm after aneurysmal subarachnoid hemorrhage (SAH) seldom have improved overall outcome; one reason may be inadequate sample size. We used data from the tirilizad trials and the Columbia University subarachnoid hemorrhage outcomes project to estimate sample sizes for clinical trials for reduction of vasospasm after SAH, assuming trials must show effect on 90-day patient-centered outcome. METHODS: Sample size calculations were based on different definitions of vasospasm, enrichment strategies, sensitivity of short- and long-term outcome instruments for reflecting vasospasm-related morbidity, different event rates of vasospasm, calculation of effect size of vasospasm on outcome instruments, and different treatment effect sizes. Sensitivity analysis was performed for variable event rates of vasospasm for a given treatment effect size. Sample size tables were constructed for different rates of vasospasm and outcome instruments for a given treatment effect size. RESULTS: Vasospasm occurred in 12% to 30% of patients. Symptomatic deterioration and infarction from vasospasm exhibited the strongest relationship to mortality and morbidity after SAH. Enriching for vasospasm by selection of patients with thick SAH slightly decreased sample sizes. Assuming beta=0.80, alpha=0.05 (2-tailed) and treatment effect size of 50%, total sample size exceeds 5000 patients to demonstrate efficacy on 3-month patient-centered outcome (modified Rankin Scale). CONCLUSIONS: Clinical trials targeting vasospasm and using traditional patient-centered outcome require very high sample sizes and will therefore be costly, time-consuming, and impractical. This will hinder development of new treatment strategies.
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 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 | 0.380 | 0.663 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".