Discrete-Time Survival Trees
Bibliographic record
Abstract
Tree-based methods are frequently used in studies with censored survival time. Their structure and ease of interpretability make them useful to identify prognostic factors and to predict conditional survival probabilities given an individual's covariates. The existing methods are tailor-made to deal with a survival time variable that is measured continuously. However, survival variables measured on a discrete scale are often encountered in practice. The authors propose a new tree construction method specifically adapted to such discrete-time survival variables. The splitting procedure can be seen as an extension, to the case of right-censored data, of the entropy criterion for a categorical outcome. The selection of the final tree is made through a pruning algorithm combined with a bootstrap correction. The authors also present a simple way of potentially improving the predictive performance of a single tree through bagging. A simulation study shows that single trees and bagged-trees perform well compared to a parametric model. A real data example investigating the usefulness of personality dimensions in predicting early onset of cigarette smoking is presented. The Canadian Journal of Statistics 37: 17-32; 2009 © 2009 Statistical Society of Canada Arbres de survie a temps discret Les methodes d'arbres sont frequemment utilisees lors d'etudes impliquant des donnees censurees. La structure d'un arbre ainsi que la facilite avec laquelle il peut etre interprete font de lui un outil utile afin d'identifier des facteurs de pronostique et de predire les probabilites de survie conditionnelles d'un individu etant donne ses covariables. Les methodes existantes ont ete developpees pour traiter une variable temporelle continue. En pratique, il arrive frequemment que la variable mesurant le temps de survie soit mesuree selon une echelle discrete. Les auteurs proposent une nouvelle methode pour construire un arbre qui est specialement adaptee aux variables de survie a temps discret. Le critere de division peut etre vu comme etant une extension, au cas de censure a droite, du critere d'entropie pour une variable categorielle. La selection de l'arbre final est basee sur une methode d'elagage combinee avec une correction bootstrap. Les auteurs presentent egalement une methode simple pour ameliorer, potentiellement, la performance d'un seul arbre avec le bagging. Une etude par simulation montre que des arbres seuls et des arbres “bagges” performent bien comparativement a un modele parametrique. Les auteurs presentent aussi une illustration de la nouvelle methode avec des vraies donnees qui investiguent l'utilite d'utiliser des dimensions de la personnalite afin de prevoir le debut de l'utilisation de la cigarette. La revue canadienne de statistique 37: 17-32; 2009 © 2009 Societe statistique du Canada
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".