Exclusion Criteria as Measurements II: Effects on Utility Functions
Bibliographic record
Abstract
Background. Researchers often justify excluding some responses in studies eliciting valuations of health states as not representing respondents’ true preferences. Here, we examine the effects of applying 8 common exclusion criteria on societal utility estimates. Setting. An online survey of a US nationally representative sample ( N = 1164) used the standard gamble method to elicit preferences for health states defined by 7 health domains from the Patient-Reported Outcomes Measurement Information System (PROMIS ® ). Methods. We estimate the impacts of applying 8 commonly used exclusion criteria on mean utility values for each domain, using beta regression, a form of analysis suited to double-bounded scales, such as utility. Results. Exclusion criteria have varied effects on the utility functions for the different PROMIS health domains. As a result, applying those criteria would have varied effects on the value of treatments (and side effects) that change health status on those domains. Limitations. Although our method could be applied to any health utility judgments, the present estimates reflect the features of the study that produced them. Those features include the selected health domains, standard gamble method, and an online format that excluded some groups (e.g., visually impaired and illiterate individuals). We also examined only a subset of all possible exclusion criteria, selected to represent the space of possibilities, as characterized in a companion article. Conclusions. Exclusion criteria can affect estimates of the societal utility of health states. We use those effects, in conjunction with the results of the companion article, to make suggestions for selecting exclusion criteria in future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.028 |
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; both teacher heads 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".