Heterogeneity and Power in Clinical Biomarker 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
PURPOSE: Many recent studies have suggested the possibility that a variety of different biomarkers may be associated with treatment outcome. However, it is also apparent that some of these biomarkers are heterogeneously distributed within a tumor. Due to this heterogeneous distribution of the biomarker, the association sought may appear weak or nonexistent. Thus, there is a wide range of conclusions in the literature on the association between a biomarker and an outcome. RESULTS: This article presents how to quantify the heterogeneity and how it influences the observed effect size and the ability to detect it (power of the study). It can be shown that the estimated effect size and the power of the study are diminished when the biomarker is measured with error. The estimated effect of the association with outcome of the average of several replicates per patient is closer to the true effect size when the number of replicates increases. CONCLUSION: The first step in designing a study of association between a biomarker and outcome is to conduct a pilot study in which several measurements per patient are taken. Based on these data, the heterogeneity of the marker within and between individuals can be estimated and used in the process of designing an appropriate study of the association between the biomarker and outcome.
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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 | 0.061 | 0.553 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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