ArticleStatistical methods in medical research2016
Power and sample size calculations for evaluating mediation effects in longitudinal studies.
Article in Statistical methods in medical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
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Who cites it
5 citing papers in PubMed.
- Endoscopic intervention in hematologic malignancy patients with severe thrombocytopenia: Methodological concerns, clinical implications, and future research directions.World journal of gastrointestinal endoscopy · 2025Article
- Rationale and Design of the Impact of Air Pollution Due to DESERT Dust in Patients with HEART Failure (DESERT HEART).Journal of clinical medicine · 2023Article
- Sample size determination for mediation analysis of longitudinal data.BMC medical research methodology · 2018Article
- Sample Size for Joint Testing of Indirect Effects.Prevention science : the official journal of the Society for Prevention Research · 2015Article
- When the test of mediation is more powerful than the test of the total effect.Behavior research methods · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Current methods of power and sample size calculations for the design of longitudinal studies to evaluate mediation effects are mostly based on simulation studies and do not provide closed-form formulae. A further challenge due to the longitudinal study design is the consideration of missing data, which almost always occur in longitudinal studies due to staggered entry or drop out. In this article, we consider the product of coefficients as a measure for the longitudinal mediation effect and evaluate three methods for testing the hypothesis on the longitudinal mediation effect: the joint significant test, the normal approximation and the test of b methods. Formulae for power and sample size calculations are provided under each method while taking into account missing data. Performance of the three methods under limited sample size are examined using simulation studies. An example from the Einstein aging study is provided to illustrate the methods.
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