Applying Mixed Regression Models to the Analysis of Repeated-Measures Data in Psychosomatic Medicine
Bibliographic record
Abstract
Objective: Although repeated-measures designs are increasingly common in research on psychosomatic medicine, they are not well suited to the conventional statistical techniques that scientists often apply to them. The goal of this article is to introduce readers to mixed regression models, which provide a more flexible and accurate framework for managing repeated-measures data. Methods and Results: We begin with a summary of the advantages that mixed regression models have over conventional statistical techniques in the context of repeated-measures designs. Next, we outline the conceptual and mathematical underpinnings of mixed regression models for a nonstatistical audience. The article ends with two examples of how these models can be applied in psychosomatic research; one deals with a prospective investigation of depressive symptoms and change in body mass index in older adults and the other with a diary study of social interactions and cortisol secretion. Conclusions: Mixed regression models offer a flexible and powerful approach to analyzing repeated-measures data. They possess important advantages over more traditional strategies, and more widespread application of these models is likely to enhance the overall quality of psychosomatic research. HLM = hierarchical linear model; OLS = ordinary least squares; ANOVA = analysis of variance; BP = blood pressure; CES-D = Center for Epidemiologic Studies–Depression; BMI = body mass index; D = dominance; EPAQ = Extended Version of the Personality Attributes Questionnaire.
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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 | 0.121 | 0.249 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".