Survival analysis: Cox proportional hazards model.
Bibliographic record
Abstract
INTRODUCTION The Cox proportional hazards model is a multivariate method used in survival analysis. Undoubtedly, many factors influence survival rates. Some of them are well documented; some are suspected but difficult to measure, such as 'clinical management' while others are unsuspected and unmeasured, and cannot be controlled except by randomization in prospective studies.' However, in analysing epidemiological data, the investigator often wishes to adjust for the effect of some variables (confounders; which are associated with the study variable or exposure and the outcome) so that the effect of other variables can be defined more clearly.' That is, multivariate analysis takes account of correlations between variables when estimating the effect of the study variable with survival period. Univariate analysis cannot allow for these correlations and, as a result, may fail to identify some factors that affect survival while falsely identifying others. For example, in a univariate analysis, a long duration of pretransplant dialysis may have an apparent beneficial effect on kidney graft survival. However, this may be entirely due to the correlation between the duration of pretransplant dialysis and the number of pretransplant transfusions. A multivariate analysis including both variables simultaneously would help distinguish the benefits of each factor.' There are two main reasons for modelling survival data. One objective is to determine which combination of potential explanatory variables affect the form of the hazard function. Another reason for modelling the hazard function is to obtain an estimate of the hazard function itself for an individual. We discuss here the concept, assumptions, analysis and interpretation of multivariate survival analysis using the Cox proportional hazards model.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 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; 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".