ArticleInternational journal of clinical biostatistics and biometrics2025
Using Previous Longitudinal Group-Randomized Rural Weight-Loss Study Data to Design a Prospective Rural Weight-Loss Trial.
Article in International journal of clinical biostatistics and biometrics, 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
5 authors.
Funding
Abstract
Background: Considerations must be taken when designing group-randomized trials due to the hierarchical structure of the data. Longitudinal group-randomized trials have an added layer of nesting adding more complexity to the study design. Simulation studies have been performed to compare the operating characteristics and validate statistical models for these hierarchical data structures, but many provide simulations from parametric distributions under set assumptions. Methods: Our manuscript aims to use previous study data to compare two statistical analysis methods in group-randomized trial designs through data-driven simulations for a prospective study design. Creating simulated datasets using existing study data from a previous study allows the existing data to drive the assumptions of the models. The motivation for this simulation study was a potential concern that our proposed longitudinal mixed-effects model could have inflated type I error. We compare the empirical power and type I error rate for our proposed model against a baseline adjusted model at a single time point when modeling a continuous outcome, % weight change at 24 months. The longitudinal model includes three follow-up time points, while the other models the outcome with an adjustment for a baseline measure, weight. The empirical power of the models is calculated and compared for varying effect sizes. Results: Results showed that the models had comparable power for the tested effect sizes and type I error rates of 3.09% and 3.87% for the longitudinal and the baseline adjusted model, respectively. Conclusion: These results show our proposed longitudinal model does not result in an inflated type I error rate and would be sufficient to use for the future trial.
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.