Advantages of mixed effects models over traditional ANOVA models in developmental studies: A worked example in a mouse model of fetal alcohol syndrome
Bibliographic record
Abstract
Developmental studies in animals often violate the assumption of statistical independence of observations due to the hierarchical nature of the data (i.e., pups cluster by litter, correlation of individual observations over time). Mixed effect modeling (MEM) provides a robust analytical approach for addressing problems associated with hierarchical data. This article compares the application of MEM to traditional ANOVA models within the context of a developmental study of prenatal ethanol exposure in mice. The results of the MEM analyses supported the ANOVA results in showing that a large proportion of the variability in both behavioral score and brain weight could be explained by ethanol. The MEM also identified that there were significant interactions between ethanol and litter size in relation to behavioral scores and brain weight. In addition, the longitudinal modeling approach using linear MEM allowed us to model for flexible weight gain over time, as well as to provide precise estimates of these effects, which would be difficult in repeated measures ANOVA.
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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 | 0.035 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".