Discussion of "Simple Defensible Sample Sizes Based on Cost Efficiency" by Peter Bacchetti, Charles E. McCulloch, and Mark R. Segal
Bibliographic record
Abstract
When introducing the notion of sample size estimation we like to share the story of the statistician who responded to the question: “How many subjects do I need for my study?” with a Socratic-like: “How many bricks does it take to build a wall?” The answer of course depends on what kind of wall one seeks to build. Bacchetti, McCulloch, and Segal (BMS) put forward a new architecture for wall building. Their paper is a welcome call to others to more explicitly consider cost perspectives when planning the size of trials. The blueprints are impressive and the wall is touted as being designed in a cost-effective manner. However, at the end of the day one is left with a nagging concern about its functionality. Different types of studies have different purposes. For example, a clinical trial might be considered either exploratory or confirmatory depending upon whether it is an early phase study to generate data that will support further investigation or a late phase study designed to corroborate promising preliminary results. The requirement for the latter is typically demanding in that its intent is to affect medical practice and a strong wall is needed to support that enterprise. As Peto, Collins, and Gray (1995) note, “The medical importance of treatment effects that are only moderate in size implies the need for large-scale randomized evidence (…). Reliable detection or refutation of moderate differences requires negligible biases and small random errors.”
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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 | 0.294 | 0.502 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.010 | 0.026 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.022 | 0.059 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".