Publishing nutrition research: a review of multivariate techniques--part 1

This article is the seventh in a series reviewing the importance of research design, analyses, and epidemiology in the conduct, interpretation, and publication of nutrition research. Although there are a variety of factors to consider before conducting nutrition research, the techniques used to conduct the statistical analysis are fundamental for translating raw data into interpretable findings. The statistical approach must be considered during the design phase of any study and often involves the use of multivariate analytical techniques. Multivariate analytical techniques represent a variety of mathematical models used to measure and quantify an exposure-disease or an exposure-outcome association, taking into account important factors that can influence this relationship. The primary purpose of this review is to introduce the more commonly used multivariate techniques, including linear and logistic regression (simple and multiple), and survival analyses (Kaplan Meier plots and Cox regression). These techniques are described in detail, providing basic definitions and practical examples with nutrition relevancy. An appreciation for the general principles within and presented previously in this article series is vital for enhancing the rigor in which nutrition-related research is implemented, reviewed, and published.