Considerations on the Use of Patient-Reported Outcomes in Comparative Effectiveness Research
Bibliographic record
Abstract
omparative effectiveness research (CER) involves studies that generate evidence through an evaluation of the spectrum of health care interventions and services that reflect patient choices for a given clinical situation, with the intent of improving patient and physician decision-making.In this paradigm, CER can be defined as a rigorous evaluation of the impact of different options that are available for treating a given medical condition for a particular set of patients.1 Such studies may compare similar treatments, such as competing drugs, or they may analyze very different approaches, like surgery and drug therapy. 1 To date, the areas of emphasis in CER have primarily been on clinical endpoints, with extensive work in mixed and indirect treatment comparisons, 2-3 use of Bayesian approaches, 4 simulated treatment comparisons, 5 realworld data use, [6][7][8] and therapeutic index determination.9 Despite the potential role of patient-reported outcomes (PRO) data in CER, a central role for PRO data has not yet been fully established in CER because of the challenges associated with the collection and interpretation of such data within and across studies.A PRO is any report on the status of a patient's health condition that comes directly from the patient.10 PRO is an umbrella term that includes a whole host of subjective outcomes, such as pain, fatigue, depression, aspects of well-being (e.g., physical, functional, psychological), treatment satisfaction, health-related quality of life, and physical symptoms, such as nausea and vomiting.11 In the traditional clinical research domain, there have been great advances with regard to the recognition of the role of PROs, 12 as evidenced also by the recent publications of guidance documents by regulatory agencies.[13][14] In different parts of the world, agencies or government bodies like the Institute for Quality and Efficiency in Healthcare (IQWiG) in Germany, the Pharmaceutical Benefits Advisory Committee (PBAC) in Australia, the National Institute of Health and Clinical Excellence (NICE) in the United Kingdom, and the Canadian Agency for Drugs and Technologies in Health (CADTH) in Canada have long histories of using PROs.While there are ongoing initiatives aimed at selecting preferred PRO instruments that would support validity and comparability of PRO measures and results, the use of PROs for CER is less defined than it is for regulatory approval.In this paper we discuss the role of PROs in CER, review the challenges associated with the inclusion of PROs in CER initiatives, provide a framework for their effective utilization, and propose several areas for future research. Role of PROs in CERAs stated by the Institute of Medicine (IOM), a primary purpose
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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 | 0.889 | 0.923 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.012 |
| Bibliometrics | 0.017 | 0.026 |
| Science and technology studies | 0.004 | 0.046 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.026 | 0.016 |
| Research integrity | 0.030 | 0.040 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".