ArticleStatistics in medicine2025
Can the Unit Size Predict Outcomes? Testing for Informativeness in Three-Level Designs.
Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Multilevel data are frequently encountered in biomedical research, and several statistical methods have been developed to analyze such data. Informativeness of the number of units on certain levels often manifests itself in multilevel data analysis and failure to account for this phenomenon will lead to biased inference. Moreover, utilizing an incorrect marginalization approach will also lead to invalid conclusions. To identify the appropriate marginal distribution to be tested in multilevel designs, we propose a sequential testing procedure to test for informativeness of unit sizes in multilevel structures with three levels. At a given level of the design, a bootstrap method is developed to estimate the null distribution of no informativeness of unit size. Simulation studies confirm the efficacy of our sequential procedure in maintaining an overall Type I error rate. Additionally, we extend our testing procedure to a multilevel regression setting, enhancing its practical applicability. We demonstrate the utility of our proposed methods through the analysis of data from a study on periodontal disease and a study on stress levels of preschoolers.
Indexed as
Identifiers
What 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.