SAMPLE SIZE ESTIMATION FOR CANCER PROGRESSION MODELS
Bibliographic record
Abstract
Human tumours are often associated with the accumulation of chromosomal alterations in the cancer cells.The identification of characteristic pathogenic routes improves prediction of survival times and optimal therapy choice.The simplest model assumes independent alterations.Then progression is measured by the count statistic, the total number of alterations.An advanced model is the oncogenetic trees mixture model.An oncogenetic tree allows both independent and sequential relationships between alterations, and the mixture model divides the patients into groups with different progression paths.Progression along such a model can be quantified univariately by the GPS (genetic progression score).On real cancer data, the GPS was shown to discriminate better than the count statistic between patient subgroups with different survival prognosis.Here, in a simulation study, we evaluate the necessary numbers of patients for detecting true relationships between genetic progression and survival time.We generate survival times correlated with count statistic and GPS, respectively.If the simple model is the correct one, misspecification with the advanced model requires about 20% larger sample size, independent from the number of events.In contrast, misspecification with the simple model leads with increasing numbers of events from 20% to 70% larger sample size.Additionally, if the true data-generating model is the mixture model, the absolute numbers are more than twice as large, thus favouring the advanced modelling approach especially in situations with limited model knowledge.
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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 | 0.048 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".