Imputation of Missing Ages in Pedigree Data
Bibliographic record
Abstract
BACKGROUND: In human pedigree data age at disease occurrence frequently is missing and is imputed using various methods. However, little is known about the performance of these methods when applied to families. In particular, there is little information about the level of agreement between imputed and actual values of temporal data and their effects on inferences. METHODS: We performed two evaluations of five imputation methods used to generate complete data for repositories to be shared by many investigators. Two of the methods are mean substitution methods, two are regression methods and one is a multiple imputation method based on one of the regression methods. To evaluate the methods, we randomly deleted the years of disease diagnosis of some men in a sample of pedigrees ascertained as part of a prostate cancer study. In the first evaluation, we used the five methods to impute the missing diagnosis years and evaluated agreement between imputed and actual values. In the second evaluation, we compared agreement between regression coefficients estimated using imputed diagnosis years with those estimated using the actual years. RESULTS/CONCLUSIONS: For both evaluations, we found optimal or near-optimal performance from a regression method that imputes a man's diagnosis year based on the year of birth and year of last observation of all affected men with complete data. The multiple imputation analogue of this method also performed well.
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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 | 0.044 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".