Theory and performance of substitution models for estimating relative causal effects in nutritional epidemiology.

ABSTRACT:

Background

Estimating relative causal effects (i.e., "substitution effects") is a common aim of nutritional research. In observational data, this is usually attempted using 1 of 2 statistical modeling approaches: the leave-one-out model and the energy partition model. Despite their widespread use, there are concerns that neither approach is well understood in practice.

Objectives

We aimed to explore and illustrate the theory and performance of the leave-one-out and energy partition models for estimating substitution effects in nutritional epidemiology.

Methods

Monte Carlo data simulations were used to illustrate the theory and performance of both the leave-one-out model and energy partition model, by considering 3 broad types of causal effect estimands: 1) direct subs

SUBMITTER: Tomova GD 

PROVIDER: S-EPMC9630885 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature