Examining the correlation between treatment effects in clinical trials and economic modeling
Bibliographic record
Abstract
Many diseases have a sequential treatment pathway. Compared with patients without previous treatment, patients who fail initial treatment may have lower success rates with a second treatment. This phenomenon can be explained by a correlation between treatment effects. We developed a statistical model of covariance for the underlying unobserved correlation between treatments and established a mathematical expression for the magnitude of the latent correlation term. We conducted a simulation study of clinical trials to investigate the correlation between two treatments and explored clinical examples based on published literature to illustrate the identification and evaluation of these correlations. Our simulation study confirmed that a treatment correlation reduces the probability of success for the second treatment, compared with no correlation. We found that treatment correlations may be observable in clinical trials, such as for depression and lung cancer, and the magnitude of correlation may be estimated. We illustrated that treatment correlations can be incorporated into an economic model, with possible impacts on cost-effectiveness results. Additional applications of correlation concepts are also discussed. We evaluated the correlation between treatment effects and our approach can be applied to clinical trial design and economic modeling of sequential clinical treatment pathways.
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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 | 0.140 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".