Estimating Long-Term Survival Rates in Patients with Anaemia of Non-Dialysis-Dependent CKD: An Expert Elicitation
Bibliographic record
Abstract
Background: Clinical trials for the treatment of anemia of CKD provide mortality data. Cost-effectiveness analyses of new treatments require survival extrapolations over a lifetime time horizon. An expert elicitation was conducted to obtain estimates of long-term survival probabilities for patients with anemia of non-dialysis dependent (NDD) CKD. Methods: Literature searches were used to identify clinical trials and observational studies that included patients with anemia of NDD CKD aged ≥ 18 years, that had > 500 participants per study arm and that reported all-cause death incidence and/or survival Kaplan-Meier (KM) curves. Study data were extracted and collated. KM curves were extrapolated to 20 years by calculating standardized mortality ratios (SMRs) compared to age- and sex-adjusted general population lifetables. A summary of relevant data was presented to six CKD experts. After an elicitation training, the experts made a judgment on the 10th, 50th and 90th percentile of 10- and 20-year survival of patients with anemia of NDD CKD. The individual judgments were combined into an overall assessment. Results: From the literature, all-cause death incidence in patients with anemia of NDD CKD was 3.5-39.4 per 100 patient-years (five studies). From SMR-extrapolated KM curves, estimated survival at 10 and 20 years was 30-57% and < 1-13%, respectively (three studies; median age of 80 and 68 years in two studies, and mean age of 66 in the third). The aggregated elicited survival values were 50% (80% confidence interval [CI]: 34-64%) at 10 years and 21% (80% CI: 8-36%) at 20 years. Conclusions: The elicited survival estimates could be used to model long-term survival and complement results from other extrapolation methods to inform and validate cost-effectiveness analyses. Long-term survival extrapolations are also likely to support patients and physicians with treatment decisions. Funding: Commercial Support - AstraZeneca
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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 | 0.158 | 0.286 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".