ArticleJournal of official statistics2008
Model Averaging Methods for Weight Trimming.
Article in Journal of official statistics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Depression may not be an independent risk factor for mortality in patients with cardiovascular disease: data from NHANES 2011-2018.Open heart · 2026Article
- Health estimate differences between six independent web surveys: different web surveys, different results?BMC medical research methodology · 2024Article
- Article
- Factors associated with sugar-sweetened beverage consumption in adults with children in the home after a statewide health communications program.BMC nutrition · 2020Article
- Time location sampling in men who have sex with men in the HIV context: the importance of taking into account sampling weights and frequency of venue attendance.Epidemiology and infection · 2018Article
- Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching.Statistical methods in medical research · 2016Article
- Estimating controlled direct effects of restrictivefeeding practices in the 'Early dieting in girls' study.Journal of the Royal Statistical Society. Series C, Applied statistics · 2016Article
- Weight trimming and propensity score weighting.PloS one · 2011Article
- Model Averaging Methods for Weight Trimming in Generalized Linear Regression Models.Journal of official statistics · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
In sample surveys where sampled units have unequal probabilities of inclusion, associations between the inclusion probabilities and the statistic of interest can induce bias. Weights equal to the inverse of the probability of inclusion are often used to counteract this bias. Highly disproportional sample designs have highly variable weights, which can introduce undesirable variability in statistics such as the population mean or linear regression estimates. Weight trimming reduces large weights to a fixed maximum value, reducing variability but introducing bias. Most standard approaches are ad-hoc in that they do not use the data to optimize bias-variance tradeoffs. This manuscript develops variable selection models, termed "weight pooling" models, that extend weight trimming procedures in a Bayesian model averaging framework to produce "data driven" weight trimming estimators. We develop robust yet efficient models that approximate fully-weighted estimators when bias correction is of greatest importance, and approximate unweighted estimators when variance reduction is critical.
Identifiers
19946471PMC2783643What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.