Profile-specific survival estimates: Making reports of clinical trials more patient-relevant
Bibliographic record
Abstract
BACKGROUND: When considering treatment options, a physician needs to know the prognosis corresponding to the risk profile of the patient seeking treatment. Reports of clinical trials generally address treatment-specific survival probabilities only in the aggregate, i.e., for the typical patient, and often express the difference in survival as a hazard ratio. Such summaries do not provide treatment-specific survival probabilities (and thus the absolute difference in these probabilities) for patient profiles that are not near the typical of those in the trial. Despite the fact that Cox intended his hazard regression method to be used to produce such profile-specific survival estimates, and even showed how to calculate them, authors are either unaware that this is possible, or else choose not to report them. PURPOSE: To illustrate how treatment- and profile-specific survival estimates are obtained from the Cox method, and can be displayed in a compact form. METHODS: We derive treatment- and profile-specific survival probabilities from the estimated survival function for the ;reference' profile. Data from the Systolic Hypertension in the Elderly Program study serve as an illustration. RESULTS: Two different formats, tabular and nomogram-based, allow the entire set of estimated treatment- and profile-specific survival probabilities to be reported. LIMITATIONS: Estimates are limited to the profiles within the covariate-space spanned by the trial, and depend on the correctness of the model. CONCLUSION: Treatment- and profile-specific survival estimates are practice-relevant, almost never reported, estimable from the Cox model, and easy to report in a compact form.
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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 | 0.587 | 0.923 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.014 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.024 | 0.032 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".