Trim and Fill¶
Duval, S., & Tweedie, R. (2000). Trim and Fill: A Simple Funnel-Plot-Based Method of Testing and Adjusting for Publication Bias in Meta-Analysis. Biometrics, 56(2), 455-463.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Silence as Signal
- The formal corrections built for meta-analysis — funnel plots, selection-model methods, trim-and-fill — transfer to adverse-event databases with the same structural assumption (recording probability depends on the unobserved true count) and the same catalogue (correct for the gradient, or move to active surveillance).
This sourceFunnel-plot and trim-and-fill corrections that detect and partially undo publication-bias-induced missingness.
- The formal corrections built for meta-analysis — funnel plots, selection-model methods, trim-and-fill — transfer to adverse-event databases with the same structural assumption (recording probability depends on the unobserved true count) and the same catalogue (correct for the gradient, or move to active surveillance).
- Unreliable Narrator
- The inversion is the correction — funnel-plot asymmetry tests, trim-and-fill, and selection-model estimators take the observed (distorted) distribution of effect sizes and reconstruct an estimate of the underlying effect.
This sourceProvides the trim-and-fill inversion that reconstructs an estimate of the underlying effect from the distorted distribution of published effect sizes.
- The inversion is the correction — funnel-plot asymmetry tests, trim-and-fill, and selection-model estimators take the observed (distorted) distribution of effect sizes and reconstruct an estimate of the underlying effect.
Verification¶
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Registry ID ref:08b3377fb443 · see in the full table