Using a Discrete Choice Experiment to Elicit Public Views Regarding Priority Setting of New Pharmaceuticals
Bibliographic record
Abstract
Background: Many countries (England, Australia) and most recently, Canada, with the initiation of the Common Drug Review process have developed committees to study whether new treatments should be reimbursed within a publicly funded health care system. The mandates of these organizations are to make funding decisions based upon evidence around effectiveness and cost-effectiveness of submitted medications. Despite the stated mandate of these organizations however, it has been noted that some medications associated with a large cost per QALY gain are funded, while others, with more attractive cost per QALY gained remain unfunded. This is not surprising given that the cost per QALY is purely concerned with a particular and limited definition of efficiency. Societies are also concerned with equity, and aspects of benefit other than those captured in the traditional QALY framework. There is little information available about what the public's preferences are with regard to priority setting for new pharmaceuticals. Thus, we conducted a discrete choice experiment (DCE) to elicit the views of the public in Alberta, Canada on what characteristics of new drugs matter in determining which new drugs should be added to the formulary. Methods: A discrete choice experiment was developed to find out the public's preferences for six features of a hypothetical new drug for a generic chronic health condition given a fixed budget. The six features of the new treatments were baseline life expectancy, gain to life expectancy, baseline quality of life (measured in QALYs), gain to quality of life (measured in QALYs), number of patients treated with the new drug, and the age group of the patients. The design accounted for potential interactions between key attributes. The survey was administered by mail, and included 10 choices as part of the DCE as well as questions about demographics, self-assessed health and prescription drug use and insurance coverage . Results: 423 surveys were returned, representing a response rate of 34%. Preliminary results indicate that all else being equal, the public would prefer to give priority to new drugs which treat a group with a higher baseline life expectancy, which provide a greater gain to life expectancy, which treat a group with a lower baseline quality of life, which provide a greater gain to quality of life, which treat more people, and which treat 35-54 year olds (compared to both younger or older patients). These indicate that people do account for characteristics other than just QALYs in determining which new drugs should be added to forumulary. Marginal rates of substitution will be presented using gain to life expectancy as a numeraire, showing how many additional life years would be needed to induce a switch to priorize a drug with less preferred characteristics. Discussion: Our study has shown the feasability of this methodology for eliciting public views for use in priority setting. The implications of this study and how this information may be used in informing decision making within drug evaluation committees will be discussed.
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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 | 0.031 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".