Two-Part Pattern-Mixture Model for Longitudinal Incomplete Semi-Continuous Toenail Data
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
Longitudinal data with true zero values, known as longitudinal semi-continuous data, frequently occur in medical, environmental and biological studies. To model longitudinal semi-continuous data, two-part modelling approaches have been widely used in literature. In the first part of the two-part model, binary logistic regression is commonly used after converting the semi-continuous responses to binary responses. In the second part, the semi-continuous data are converted to positive continuous data after removing the true zero values from the responses. Although positive continuous or non-zero values tend to show a positively skewed distribution pattern, in the literature the normal distribution is commonly used to model them. Also, in longitudinal studies, data often suffer individual dropouts as they are collected overtime. In this paper, we propose a two-part pattern-mixture model to analyze longitudinal semi-continuous data with dropouts. In the proposed approach, we use pattern-mixture binary mixed models for the first part and positively continuous pattern-mixture gamma mixed models for the second part. Our approach can accommodate both subject- and time-specific correlation as well as dropout pattern. We also incorporate a computationally efficient estimation method for our models using a penalize quasi-likelihood approach. The proposed method is illustrated with an application to the longitudinal incomplete toenail data
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.006 |
| 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 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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