Are Pharmaceuticals Cost-Effective? A Review Of The Evidence
Bibliographic record
Abstract
PROLOGUE: A study of prescription drugs' cost-effectiveness might seem like an arcane research subject, but in fact it lies at the heart of the current debate over the growing use (and rising cost) of prescription drugs. Advocates argue that drugs offer value for the money, substituting for more costly and invasive treatments and hospitalizations. This study offers a cogent framework in which to determine prescription drugs' value in making treatment decisions. It is based on cost-utility analysis, which measures benefit in terms of quality-adjusted life years gained by drug therapies. Although, as the authors note, the approach has some limitations, such a measure enables comparisons across diverse conditions and allows the measure of benefit to take into account the value different people place on their treatment options. Peter Neumann is an assistant professor of policy and decision sciences in the Department of Health Policy and Management, Harvard School of Public Health (HSPH). He is deputy director of the Program on the Evaluation of Medical Technology there. Eileen Sandberg is a doctoral candidate in health policy at Harvard. Chaim Bell is a practicing internist and a doctoral candidate at the University of Toronto in Ontario, Canada; at the time of this study he was a visiting fellow at the HSPH. Patricia Stone is an assistant professor in the University of Rochester's School of Nursing and Department of Community and Preventive Medicine. She was an HSPH postdoctoral fellow. Richard Chapman is also a doctoral candidate in health policy at Harvard; he has worked at the Harvard Center for Risk Analysis, helping to develop a comprehensive database for cost-utility analyses. ABSTRACT: The argument that prescription drugs are cost-effective has been made both by the pharmaceutical industry to support rising drug prices and expenditures, and by advocates of expanded drug coverage for elderly and low-income persons. A new database of 228 published cost-utility analyses sheds light on the issue. According to published data, some drugs do save money or are cost-effective, but the issue depends critically on the context in which the drug is used and the intervention with which it is compared. Cost-utility analyses funded by the drug industry tend to report more favorable results than do those funded by nonindustry sources. Cost-effectiveness analysis can help policymakers to determine whether drugs and other interventions offer value for money.
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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 | 0.016 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| 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".