Application of Bayesian variable selection methods and shrinkage priors to epidemiological data
Bibliographic record
Abstract
First developed in the 1970s, variable selection plays a vital role in selecting the correct inclusion of variables in a prediction model. From forward selection to penalized regression, a great number of approaches have been constructed within the frequentist framework. More recently, Bayesian variable selection techniques have been developed and gained popularity because of their ability to learn from prior knowledge and construct credible intervals for the parameters without additional computations. However, the application of the Bayesian techniques on data with missingness or spatial information have been limited and the use of customizable programs such as JAGS and RStan are required.In this thesis, we provide a comprehensive review of some commonly-used variable selection methods, especially the Bayesian priors, and the comparisons of these methods from past literature. We then apply the regularized Horseshoe prior to two epidemiological datasets to investigate: (1) the random effect of health care regions and social-material deprivation on the treatment decision for patients with aortic stenosis, and (2) the correlations between epidemiological factors and HIV status obtained from a HIV self-testing arm survey. The shrinkage prior is first applied to the AS treatment dataset from the Institut national de santé publique du Québec (INSPQ). Spatial information is contained in postal codes and treated as random effects with 0-1 adjacency matrix. We then analyze the HIV data obtained from a quasi-randomized control trial to explore the risk factors of human immunodeficiency virus (HIV) status. With missing values, we apply a Bayesian hierarchical model for imputation and the shrinkage prior to drop irrelevant predictors. At last, we provide our code and offer ideas for future research
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How this classification was reachedexpand
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 | 0.003 | 0.019 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".