Abstract TP380: Increasing the Sample Size in the Secondary Prevention of Small Subcortical Strokes (SPS3) Trial: What Did We Gain?
Bibliographic record
Abstract
Introduction: The NIH-funded SPS3 study assessed whether combination antiplatelet therapy (AC) was superior to aspirin alone (AP) in preventing recurrent stroke among patients with a lacunar stroke at baseline. Due to lower than expected overall rates of recurrent stroke, the sample size was increased from 2500 to 3000 to ensure adequate power to detect a 25% reduction in recurrent stroke. The antiplatelet trial was terminated early due to lack of efficacy, coupled with safety concerns. We conducted a retrospective analysis to assess the gains achieved by the sample size re-estimation. Methods: We assessed the difference in stroke recurrence among the first 2500 patients recruited, assuming follow-up ended when the antiplatlet arm terminated, and compared the results to those obtained with the complete 3020 patients. Results: Among the first 2500 patients, we observed 248 events, over an average of 3.9 years follow-up. The recurrent stroke rate for the AC and AP groups was 2.5% and 2.7% per year, respectively. The hazard ratio for AC vs. AP was 0.92 (95% CI: 0.72-1.18). Including the additional 520, only 15 more patients had primary events, and recurrent stroke rates (2.7%, and 2.5% year), and the HR of 0.92 (95% CI: 0.72-1.16) were similar. Conclusions: Despite an increase in sample size in SPS3, there was no appreciable difference in the results obtained for the antiplatelet trial had the study adjustment not been made. While sample size re-estimation is an important tool for ensuring adequate power for detecting treatment effects of interest, there is a potential to add unnecessary cost and complexity when no effect exists. More post-hoc examinations of studies using adaptive designs are warranted to understand their strengths and weaknesses.
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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 | 0.057 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".