Statistical inference for the randomized play the winner design
Bibliographic record
Abstract
In clinical tlials rvith extleme outcomes, it is ethically desilable to treat as many patients as possible rvith the superior tleatment.Adaptive designs accomplish this ivhile still producing statistically meaningful results.One such design is knorvn as the landomized play the rvinner lule (RPWR).Trvo asymptotic methods fol consttucting confidence intervals based on data from the RPWR ale plesented and compaled by simulation.It is found that both methods pelform ri'ell fol small sample sizes despite being approximate methods.Some othel aspects of the RPWR are examined, sucli as the rate of convelgence of a martingale central limit theorem and sorne appealing ploperties of the allocation probabilities.undelgraduate student lesearch arvard.If it were not for him, this thesis rvould not be possible.Even when my lesearch seened like it rvas not leading an¡vhele he kept encouraging me.His comments and suggestions ivere greatly appleciated.
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 | 0.156 | 0.359 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".