Advancing the methods and accessibility of cost-effectiveness and value of information analyses in health care
Bibliographic record
Abstract
This thesis comprises three methodological advancements that address important issues related to cost-effectiveness analysis (CEA) and expected value of information (EVI) analysis in health technology assessment. Aims: 1) To develop a practical sampling scheme for the incorporation of external evidence in CEAs conducted alongside randomized controlled trials (RCT); 2) To develop non-parametric methods for the calculation of the expected value of sample information (EVSI) for RCT-based CEAs; 3) To develop a computationally efficient algorithm for the calculation of single-parameter expected value of partial perfect information (EVPPI) for RCT-based and model-based CEAs. The theories and methods laid out in this work are accompanied by real-world CEA and EVI analyses of the Canadian Optimal Therapy of Chronic Obstructive Pulmonary Diseases (OPTIMAL) trial, a RCT on combination pharmaceutical therapies in chronic obstructive pulmonary diseases (COPD). Results: 1) The ‘vetted bootstrap’ is a semi-parametric algorithm based on rejection sampling and bootstrapping that allows the incorporation of external evidence into RCT-based CEAs. Implementing this method to incorporate external information on the effect size of treatment in the OPTIMAL trial required only minor modifications to the original CEA algorithm. 2) A Bayesian interpretation of the bootstrap allows non-parametric calculation of EVSI through two-level resampling. In the case study, incorporation of missing value imputation and adjustment for covariate imbalance in EVI calculations generated EVSI and the expected value of perfect information (EVPI) values that were significantly different than those calculated conventionally, demonstrating the flexibility of this method and the potential impact of modeling such aspects of the analysis on EVI calculations. 3) The new method enabled the calculation of EVPPI for the effect size of treatment for the exemplary RCT data, and showed a significant (up to 25 times in terms of root-mean-squared error) improvement in efficiency compared to the conventional EVPPI calculation methods in a series of simulations. Summary: This thesis provides several original advancements in the methodology of the CEA and EVI analysis of RCTs and enables several analytical approaches that have hitherto been available only through parametric modeling of RCT data.
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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 | 0.244 | 0.628 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".