A Comparison of Statistical Models and Deep Learning for Data with Binary Response and Longitudinal Covariates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In statistics, longitudinal data refers to data in which the response variable and explanatory variables are measured several times for each subject. However, in the machine learning literature, longitudinal data can also refer to data in which only the explanatory variables are repeatedly measured, but not the response variable. This thesis compared two statistical models - the baseline logistic regression and the two-stage joint model, and two neural network approaches - the feed-forward neural network and the recurrent neural network with long short-term memory, in terms of the prediction sensitivity, specificity, area under the receiver operating characteristic curve, and Brier score. Data analysis was conducted using data from two clinical trials and a simulation study was also conducted. For the datasets generated and studied in this thesis, the neural network approaches show no advantages compared to the other statistical methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it