Sample size calculations for continuous outcomes in clinical nutrition.
Christian RitzMette Frahm OlsenBenedikte GrenovHenrik FriisPublished in: European journal of clinical nutrition (2022)
In nutrition research, sample size calculations for continuous outcomes are important for the planning phase of many randomized trials and could also be relevant for some observational studies such as cohort and cross-sectional studies. However, only little literature dedicated to this topic exists within nutritional science. This article reviews the most common methods for sample size calculations in nutrition research. Approximate formulas are used for explaining concepts and requirements and for working through examples from the literature. Sample size calculations for the various study designs, which are covered, may all be seen as extensions of the sample size calculation for the basic two-group comparison through the application of suitable scaling factors and, possibly, modification of the significance level. The latter is needed for sample size calculations for multi-group designs and designs involving multiple primary outcomes. Like cluster-randomized designs, these types of study designs may be more challenging than standard sample size calculations. In such non-standard scenarios, there may be a need for consulting a biostatistician. Finally, it should be stressed that there may be many ways to plan a study. The final sample size calculation provided for a grant applicant, study protocol, or publication will often not only depend on considerations and input information as described in this article but will also involve restrictions in terms of logistics and/or resources.
Keyphrases
- density functional theory
- molecular dynamics
- molecular dynamics simulations
- monte carlo
- cross sectional
- systematic review
- physical activity
- randomized controlled trial
- public health
- type diabetes
- double blind
- climate change
- healthcare
- social media
- skeletal muscle
- finite element analysis
- health information
- placebo controlled