Evaluating therapeutic effect on WOMAC subscales in osteoarthritis RCTs: When model choice matters
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: The study aimed at developing a method for modelling the Western Ontario and McMaster Universities index (WOMAC), accounting for correlation between its subscales and for heterogeneity of treatment effect (HTE), using data from 2 twin trials on knee osteoarthritis. METHOD: Two randomized, double-blind, placebo-controlled clinical trials (twin trials). Studies aimed at investigating the effectiveness of a pharmacological treatment on clinical outcomes of knee osteoarthritis, measured using WOMAC index. To take into account that the WOMAC subscales are correlated and skewed, we proposed and compared multivariate gamma and Gaussian approaches with latent variable capturing correlation between outcomes. Besides the latent term, the interaction between the latent term and treatment, accounting for HTE, was further estimated. RESULTS: Modelling the subscales by using a gamma approach accounting for skewness of data, we found out different results compared with Gaussian models. The main difference regarded the latent variable interacting with treatment (accounting for unobserved heterogeneity), which is not significant for the Gaussian approach (P value = .102) and significant in the gamma model (P value < .002). Thus, indicating that unobserved covariates affect treatment's performance. Additionally, plotting the observed and the estimated values of WOMAC index of the Gaussian and gamma models, we showed that, compared with the Gaussian, the gamma one best fits the data, especially among poor responders. CONCLUSION: Multivariate gamma approach accounting for correlation between outcomes and for HTE has been demonstrated to be more suitable to model WOMAC subscales and to provide more information on effect of therapy.
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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 | 0.209 | 0.319 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".