ArticleHuman vaccines & immunotherapeutics2019
Comparison of three sample size calculation methods for non-inferiority vaccine trials with multiple continuous co-primary endpoints.
Article in Human vaccines & immunotherapeutics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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4 citing papers in PubMed.
- Seamless phase 2/3 design for trials with multiple co-primary endpoints using Bayesian predictive power.BMC medical research methodology · 2024Trial
- Prospective, monocentric observational study on the clinical use and patient satisfaction of an implantable venous access Port.Langenbeck's archives of surgery · 2025Observational
- Cost, time savings and effectiveness of wearable devices for remote monitoring of patient rehabilitation after total knee arthroplasty: study protocol for a randomized controlled trial.Journal of orthopaedic surgery and research · 2023Article
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5 authors.
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Abstract
Clinical trials that study immunogenicity of combination vaccines often have less power than desirable. To make up for the reduction in statistical power at the study level, researchers have to increase the study sample size. To study immunogenicity variables, we used the geometric mean concentration of immune response after vaccination as immunologic endpoint and compared 3 sample size calculation methods: the "Inflation factors" method, the "Incrementing" method, and the "Bonferroni correction" method when there are multiple continuous co-primary endpoints. The parameters were set according to the actual situation of the use of combination vaccines and the simulation results were used as reference. The present study demonstrates that all 3 methods are applicable when the effect size of each endpoint is similar and the endpoints are at most weakly correlated, but when there is a true difference in effect sizes among endpoints, the "Incrementing" method has the best performance.
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