Using propensity score methods to analyse individual patient-level cost-effectiveness data from observational studies
Bibliographic record
Abstract
The methodology relating to the statistical analysis of individual patient-level cost-effectiveness data collected alongside randomised controlled trials (RCTs) has evolved dramatically in the last ten years. This body of techniques has been developed and applied mainly in the context of the randomised clinical trial design. There are, however, many situations in which a trial is neither the most suitable nor the most efficient vehicle for the evaluation. This paper provides a tutorial-like discussion of the ways in which propensity score methods could be used to assist in the analysis of observational individual patient-level cost-effectiveness data. As a motivating example, we assessed the cost-effectiveness of CABG versus PTCA – one year post procedure - in a cohort of individuals who received the intervention within 365 days of their index admission for AMI. The data used for this paper were obtained from the Ontario Myocardial Infarction Database (OMID), linking these with data from the Canadian Institute for Health Information (CIHI), the Ontario Health Insurance Plan (OHIP), the Ontario Drug Benefit (ODB) program, and Ontario Registered Persons Database (RPDB). We discuss three ways in which propensity score can be used to control for confounding in the estimation of average cost-effectiveness, and provide syntax codes for both propensity score matching and cost-effectiveness modelling.
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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 | 0.107 | 0.316 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".