Statistical Issues on Analysis of Censored Data Due to Detection Limit
Bibliographic record
Abstract
Measures of substance concentration in urine, serum or other biological matrices often have an assay limit of detection. When concentration levels fall below the limit, the exact measures cannot be obtained, and thus are left censored. Common practice for addressing the censoring issue is to delete or 'fill-in' the censored observations in data analysis, which often results in biased or non-efficient estimates. Assuming the concentration or transformed concentration follows a normal distribution, a Tobit regression model can be applied. When the study population is heterogeneous, for example due to the existence of a latent group of subjects who lack the substance, the problem becomes more challenging. In this paper, we conduct intensive simulation studies to investigate the statistical issues in analyzing censored data and compare different methods in which the data are treated either as a dependent variable or an independent variable. We also analyze triclosan data in the NHANES study and metabolites data in the Bogalusa Heart Study to illustrate the issues. Some guidelines for analyzing such censored data are provided.
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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 | 0.413 | 0.705 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".