Methods to improve the estimation of time‐to‐event outcomes when data is de‐identified
Bibliographic record
Abstract
Technological advancements in recent years have sparked the use of large databases for research. The availability of these large databases has administered a need for anonymization and de-identification techniques, prior to publishing the data. This de-identification alters the data, which in turn can impact the results derived post de-identification and potentially lead to false conclusions. The objective of this study is to investigate if alterations to a de-identified time-to-event data set may improve the accuracy of the estimates. In this data set, a missing time bias was present among censored patients as a means to preserve patient confidentiality. This study investigates five methods intended to reduce the bias of time-to-event estimates. A simulation study was conducted to evaluate the effectiveness of each method in reducing bias. In situations where there was a large number of censored patients, the results of the simulation showed that Method 4 yielded the most accurate estimates. This method adjusted the survival times of censored patients by adding a random uniform component such that the modified survival time would occur within the final year of the study. Alternatively, when there was only a small number of censored patients, the method that did not alter the de-identified data set (Method 1) provided the most accurate estimates.
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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 | 0.045 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".