Prioritizing Research in an Era of Personalized Medicine: The Potential Value of Unexplained Heterogeneity
Bibliographic record
Abstract
BACKGROUND: Clinical care is moving from a "one size fits all" approach to a setting in which treatment decisions are based on individual treatment response, needs, preferences, and risk. Research into personalized treatment strategies aims to discover currently unknown markers that identify individuals who would benefit from treatments that are nonoptimal at the population level. Before investing in research to identify these markers, it is important to assess whether such research has the potential to generate value. Thus, this article aims to develop a framework to prioritize research into the development of new personalized treatment strategies by creating a set of measures that assess the value of personalizing care based on a set of unknown patient characteristics. METHODS: Generalizing ideas from the value of heterogeneity framework, we demonstrate 3 measures that assess the value of developing personalized treatment strategies. The first measure identifies the potential value of personalizing medicine within a given disease area. The next 2 measures highlight specific research priorities and subgroup structures that would lead to improved patient outcomes from the personalization of treatment decisions. RESULTS: We graphically present the 3 measures to perform sensitivity analyses around the key drivers of value, in particular, the correlation between the individual treatment benefits across the available treatment options. We illustrate these 3 measures using a previously published decision model and discuss how they can direct research in personalized medicine. CONCLUSION: We discuss 3 measures that form the basis of a novel framework to prioritize research into novel personalized treatment strategies. Our novel framework ensures that research targets personalized treatment strategies that have high potential to improve patient outcomes and health system efficiency. HIGHLIGHTS: It is important to undertake research prioritization before conducting any research that aims to discover novel methods (e.g., biomarkers) for personalizing treatment.The value of unexplained heterogeneity can highlight disease areas in which personalizing treatment can be valuable and determine key priorities within that area.These priorities can be determined under assumptions of the magnitude of the individual-level treatment effect, which we explore in sensitivity analyses.
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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 | 0.162 | 0.305 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".