Accuracy Loss Due to Selection Bias in Cohort Studies with Left Truncation
Bibliographic record
Abstract
BACKGROUND: Selection is a common problem in paediatric and perinatal epidemiology, and truncation can be thought of as missing person time that can result in selection bias. Left truncation, also known as late or staggered entry, may induce selection bias and/or adversely affect precision. There are two kinds of left truncation: fixed left truncation where the start of follow-up is initiated at a set time, and variable left truncation where follow-up begins at a stochastically varying time-point. METHODS: Using data from a time-to-pregnancy study, augmented by a simulation study, we demonstrate the effects of fixed and variable truncation on estimates of the hazard ratio. RESULTS: First, fixed or variable non-differential left truncation results in a loss of precision. Fixed or variable differential left truncation results in a bias either towards or away from the null as well as a loss of precision. The extent and direction of this bias is a function of the size and direction of the association between exposure and outcome, and occurs in common scenarios and under a wide range of conditions. CONCLUSIONS: As demonstrated in simulation studies, selection bias due to left truncation could have a serious impact on inferences, especially in the case of fixed or variable differential left truncation. When present in epidemiologic studies, proper accounting for left truncation is just as important as proper accounting for right censoring.
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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 | 0.249 | 0.561 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".