Calculating Utility Decrements Associated With an Adverse Event
Bibliographic record
Abstract
BACKGROUND: When calculating the decreases in health utility associated with adverse events, often a number of respondents achieve the upper utility bound of 1. "Marginal" Tobit or CLAD coefficients have been used to account for this. These are calculated by using a Tobit or a CLAD model to estimate the decrease in a latent unbounded variable associated with the event or condition, then to multiply by the proportion of respondents falling below 1 in order to transform back to the utility scale. OBJECTIVE: & METHODS: Starting with the Tobit model, we show mathematically that this procedure is not valid, when calculating decreases in utility associated with binary events. We then generalize the result to the CLAD model. A selection of published studies is used to illustrate the bias in the marginal Tobit decrements. RESULTS: The degree of bias is more severe the greater the decrease in utility associated with the event, and the larger the proportion of individuals at the upper ceiling.In the examples studied, the degree of bias was often greater than 10%. We provide the correct formula for calculating the utility decrement. CONCLUSIONS: The marginal Tobit and CLAD coefficients should not be used as estimates of a utility decrement corresponding to an adverse event or health condition unless the coefficients are small in absolute value, or if the proportion of individuals at the upper utility bound is small. In other settings, the corrected formula or alternative regression methods (e.g. linear models of mean utility) should be considered.
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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 | 0.025 | 0.023 |
| 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) | 0.007 | 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 teacher head, 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".