Multistage designs in the genomic era: Providing balance in complex disease studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this summary paper, we describe the contributions included in the Multistage Design group (Group 14) at the Genetic Analysis Workshop 15, which was held during November 12-14, 2006. Our group contrasted and compared different approaches to reducing complexity in a genetic study through implementation of staged designs. Most groups used the simulated dataset (problem 3), which provided ample opportunities for evaluating various staged designs. A wide range of multistage designs that targeted different aspects of complexity were explored. We categorized these approaches as reducing phenotypic complexity, model complexity, analytic complexity or genetic complexity. In general we learned that: (1) when staged designs are carefully planned and implemented, the power loss compared to a single-stage analysis can be minimized and study cost is greatly reduced; (2) a joint analysis of the results from each stage is generally more powerful than treating the second stage as a replication analysis.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it