<b>FamEvent</b>: An <i>R</i> Package for Generating and Modeling Time-to-Event Data in Family Designs
Bibliographic record
Abstract
is a comprehensive R package for simulating and modelling age-at-disease onset in families carrying a rare gene mutation. The package can simulate complex family data for variable time-to-event outcomes under three common family study designs (population, high-risk clinic and multi-stage) with various levels of missing genetic information among family members. Residual familial correlation can be induced through the inclusion of a frailty term or a second gene. Disease-gene carrier probabilities are evaluated assuming Mendelian transmission or empirically from the data. When genetic information on the disease gene is missing, an Expectation-Maximization algorithm is employed to calculate the carrier probabilities. Penetrance model functions with ascertainment correction adapted to the sampling design provide age-specific cumulative disease risks by sex, mutation status, and other covariates for simulated data as well as real data analysis. Robust standard errors and 95% confidence intervals are available for these estimates. Plots of pedigrees and penetrance functions based on the fitted model provide graphical displays to evaluate and summarize the models.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.110 | 0.037 |
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".