Open AccessThe possibilities of the use of N-of-1 and do-it-yourself trials in nutritional research
Author(s) -
Tanja Krone,
Ruud Boessen,
Sabina Bijlsma,
Robin van Stokkum,
Nard D. S. Clabbers,
Wilrike J. Pasman
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0232680
Subject(s) - frequentist inference , bayesian probability , statistical inference , computer science , sample size determination , inference , mixed model , linear model , sensitivity (control systems) , bayes' theorem , bayesian inference , population , statistics , machine learning , artificial intelligence , mathematics , medicine , environmental health , electronic engineering , engineering
Background N-of-1 designs gain popularity in nutritional research because of the improving technological possibilities, practical applicability and promise of increased accuracy and sensitivity, especially in the field of personalized nutrition. This move asks for a search of applicable statistical methods. Objective To demonstrate the differences of three popular statistical methods in analyzing treatment effects of data obtained in N-of-1 designs. Method We compare Individual-participant data meta-analysis, frequentist and Bayesian linear mixed effect models using a simulation experiment. Furthermore, we demonstrate the merits of the Bayesian model including prior information by analyzing data of an empirical study on weight loss. Results The linear mixed effect models are to be preferred over the meta-analysis method, since the individual effects are estimated more accurately as evidenced by the lower errors, especially with lower sample sizes. Differences between Bayesian and frequentist mixed models were found to be small, indicating that they will lead to the same results without including an informative prior. Conclusion For empirical data, the Bayesian mixed model allows the inclusion of prior knowledge and gives potential for population based and personalized inference.