Robust Estimation of State Occupancy Probabilities for Interval-Censored Multistate Data: An Application Involving Spondylitis in Psoriatic Arthritis
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
Abstract We formulate a three-state illness-death model to estimate the proportion of psoriatic arthritis patients developing spondylitis over time. Data from a longitudinal cohort of patients are available but the transitions in this model are interval-censored for the onset of spondylitis; times of deaths are right-censored. Robust methods for estimating the prevalence of spondylitis over time are described based on differences in marginal survivor functions for state entry times in the spirit of Pepe et al. (Citation1991). Nonparametric estimates (Turnbull, Citation1976) and local likelihood estimates (Loader, Citation1999) of the marginal distributions are derived. Multiplicative intensity Markov regression models are used to examine covariate effects. Keywords: Multistate analysisPsoriatic arthritisRobust estimationSpondylitisState occupancy probabilityMathematics Subject Classification: Primary 62N01, 62M05Secondary 62P10 Acknowledgments This research was supported by grants from the Natural Sciences and Engineering Research Council of Canada and the Canadian Institutes of Health Research. The authors thank Dr. Dafna Gladman for permission to use the data from the University of Toronto Psoriatic Arthritis Clinic, and Drs. Dafna Gladman and Vinod Chandran for helpful discussions. R. J. Cook is Canada Research Chair in Statistical Methods for Health Research. Notes † p-value for test of common covariate effect on0 → 2 and1 → 2 transitions. † p-value for test of common covariate effect on0 → 2 and1 → 2 transitions.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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