Appropriate within-subjects statistical models for the analysis of baroreflex sensitivity
Bibliographic record
Abstract
An individuals baroreflex sensitivity is typically described by the relationship between serial manipulations in systolic blood pressure and the changes in pulse interval. Although this experimental approach is essentially within-subjects in nature, least squares regression (LSR) analysis is typically employed by researchers to derive sensitivity slopes (gains) for individual subjects. These individual gains are then pooled as summary measures for various samples or experimental conditions. We highlight that the underlying assumption for LSR of case independence is violated with such an approach, resulting in possible estimation biases and compromised statistical power. Using a typical data set, we introduce more appropriate analyses based on the linear mixed model, which takes into account the correlated nature of the data at the individual subject level. We encourage researchers to consider the linear mixed model approach because it is more efficient, in that the whole data set is analysed in one step, is associated with less bias and results in greater statistical power compared with conventional analyses of baroreflex sensitivity for samples of subjects.
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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 | 0.068 | 0.120 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".