Patient Preferences in Targeted Pharmacotherapy for Cancers: A Systematic Review of Discrete Choice Experiments
Bibliographic record
Abstract
BACKGROUND: Targeted pharmacotherapy has been increasingly applied in cancer treatment due to its breakthroughs. However, the unmet needs of cancer patients are still significant, highlighting the urgency to investigate patient preferences. It is unclear how patients deliberate their choices between different aspects of targeted therapy, including cost, efficacy, and adverse events. Since discrete choice experiments (DCEs) have been widely applied to patient preference elicitation, we reviewed DCEs on targeted therapy for different cancers. We also synthesized evidence on the factors influencing patients' choices and their willingness-to-pay (WTP) for survival when treated by targeted therapy. METHODS: We searched databases, including PubMed, EMBASE and MEDLINE, up to August 16, 2022, supplemented by a reference screening. The attributes from the selected studies were categorized into three groups: outcomes, costs, and process. We also calculated the relative importance of attributes and WTP for survival whenever possible. The purpose, respondents, explanation, findings, significance (PREFS) checklist was used to evaluate the quality of the included DCE studies. RESULTS: The review identified 34 eligible studies from 13 countries covering 14 cancers, such as breast, ovarian, kidney, prostate, and skin cancers. It also reveals a rising trend of DCEs on this topic, as most studies were published after 2018. We found that patients placed higher weights on the outcome (e.g., overall survival) and cost attributes than on process attributes. On average, patients were willing to pay $561 (95% confidence interval [CI]: $415-$758) and $716 (95% CI $524-$958) out-of-pocket for a 1-month increase in progression-free survival and overall survival, respectively. PREFS scores of the 34 studies ranged from 2 to 4, with a mean of 3.38 (SD: 0.65), suggesting a reasonable quality based on the checklist. However, most studies (n = 32, 94%) did not assess the impact of non-responses on the results. CONCLUSIONS: This is the first systematic review focusing on patient preferences for targeted cancer therapy. We showcased novel approaches for evidence synthesis of DCE results, especially the attribute relative importance and WTP. The results may inform stakeholders about patient preferences toward targeted therapy and their WTP estimates. More studies with improved study design and quality are warranted to generate more robust evidence to assist decision making.
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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 | 0.046 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".