Multitype Events and the Analysis of Heart Failure Readmissions
Bibliographic record
Abstract
BACKGROUND: Heart failure-related hospital readmissions and mortality are often outcomes in clinical trials. Patients may experience multiple hospital readmissions over time with mortality acting as a dependent terminal event. Univariate composite end points are used for the analysis of readmissions. We may amend these approaches to include emergency department visits as a further outcome. An alternative multivariate modeling approach that categorizes hospital readmissions and emergency department visits as separate event types is proposed. METHODS AND RESULTS: We seek to compare the modeling approach which handles event types as separate, correlated end points against composites that amalgamate them to create a unified end point. Using a heart failure data set for illustration, a model with random effects for event types is estimated. The time-to-first event, unmatched win-ratio, and days-alive-and-out-of-hospital composites are derived for comparison. The model provides supplementary statistics such as the correlation among event types and yields considerably more power than the competing composite end points. CONCLUSIONS: The effect on individual outcomes is lost when they are intermingled to form a univariate composite. Simultaneously modeling different outcomes provides an alternative or supplementary analysis that may yield greater statistical power and additional insights. Improvements in software have made the multitype events model easier to implement and thus a useful, more efficient option when analyzing heart failure hospital readmissions and emergency department visits.
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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 | 0.037 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".