Spatio–temporal modeling for disease mapping using CAR and B‐spline smoothing
Bibliographic record
Abstract
In this paper, generalized additive mixed models are constructed for the analysis of geographical and temporal variability of disease ratios. In this class of models, spatio–temporal models that use conditionally autoregressive smoothing across the spatial dimension and B‐spline smoothing over the temporal dimension are considered. The frequentist analysis of these complex models is computationally difficult. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of complex models computationally convenient. Recently developed data cloning (DC) method provides a frequentist approach to mixed models and equally computationally convenient. We propose to use DC, which yields to maximum likelihood estimation, to conduct frequentist analysis of spatio–temporal modeling of disease ratios. The advantages of DC approach are that the non‐estimable parameters are flagged automatically and prediction (and prediction intervals) of the smoothing incidence ratios over space and time are easily obtained. We illustrate this approach using a real dataset of yearly childhood asthma visits to hospital in the province of Manitoba, Canada, during 2000–2010. The performance of the DC approach is also studied through a simulation study. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".