Preventing Osteoporotic Fractures in Men Living with HIV: Model Calibration and Economic Evaluation Studies
Bibliographic record
Abstract
I aimed to provide Ontario’s public healthcare payers with an economic evaluation of fracture prevention strategies for HIV-positive men who take antiretroviral therapy (ART). When developing microsimulations models for this purpose, calibration is critical and computationally expensive. Studies that explore efficient calibration methods for microsimulation models are limited. As such, I sought to determine whether simulated annealing or Nelder-Mead is the best-performing parameter search algorithm to calibrate a microsimulation model in project one; determine whether a discrete-event simulation model of fractures leads to more efficient calibrations than a patient-level, discrete-time simulation model in project two; and, estimate the cost-effectiveness of osteoporosis screening and treatment strategies in ART-treated men over a lifetime horizon in project three. For projects one and two, I constructed a Markov microsimulation model that tracked bone loss and incident fractures. All calibrations were based on 4 calibration targets, 12 calibration inputs, a binomial log-likelihood goodness-of fit measure, and first-order searches in a simulated sample of 1000. I assessed calibration performance according to differences in the goodness-of-fit and the time to identifying a good parameter set. In project one, both algorithms produced good sets that had similar fit, but simulated annealing identified the sets two times faster than Nelder-Mead. In project two, both model structures generated similarly accurate good sets, while the discrete-event simulation generated these three times as quickly as the discrete-time simulation. In project three, I evaluated 13 strategies for initiating osteoporosis treatment based on Fracture Risk Assessment Tool (FRAX) scores, with and without bone mineral density (BMD). The base-case included 50-year-old, HIV-positive men who took ART for ≥3 years and did not have a history of fractures or osteoporosis treatment. I incorporated the calibrated results from projects 1 and 2 in project 3’s probabilistic analysis. I discounted outcomes annually by 1.5%. At a willingness-to-pay threshold of $50,000/QALY, it is most cost-effective to assess FRAX without BMD, offer treatment if FRAX is ≥10%, and re-screen annually. Collectively, these studies suggest using simulated annealing and discrete-event simulation as efficient calibration methods; as well as that the payer should not pay for BMD screening in older HIV-positive men.
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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 | 0.029 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".