Appraisal of models for the study of disease progression in psoriatic arthritis.
Bibliographic record
Abstract
The subject of the thesis is the use of models for disease progression in arthritis, with special emphasis on Markov regression models. The first objective of the thesis is to propose a Pearson type goodness of fit test for stationary Markov models with covariates. The grouping technique proposed by Hosmer and Lemeshow for logistic regression models is extended to models with response variables recorded repeatedly over time. This generalization is particularly appropriate for panel data in which different numbers of observations, unequally spaced, are obtained for each sampling unit. Due to the complexity of the theoretical distribution of the test statistic, bootstrap methodology is used to calculate the distribution of the statistic under the null hypothesis. The power of the goodness of fit test is investigated for a particular model using a nested bootstrap algorithm. The proposed test is applied to a data set obtained at the University of Toronto with the objective of identifying prognostic factors for disease progression in psoriatic arthritis (PsA), measured via the number of damaged joints. As the Markov regression model does not fit the PsA data, the second objective of the thesis is to consider potentially better models. A larger data set is analysed for this purpose . Additionally, neither the outcome variable nor the covariates are categorized. Two mixture regression models for longitudinal data are examined to determine if there is statistical evidence supporting the hypothesis that a proportion of individuals never develop damaged joints. The results indicate that a negative binomial regression model without added zeros might provide a reasonable approach. The goodness of fit of this model is examined using bootstrap methodology, comparable to that used for the Markov regression model.
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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 | 0.023 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".