Dataset Information

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 substitutions of the exposure with a single component, 2) inadvertent substitutions of the exposure with several components, and 3) average relative causal effects of the exposure instead of all other dietary sources. Models containing macronutrients, foods measured in calories, and foods measured in grams were all examined.

Results

The leave-one-out and energy partition models both performed equally well when the target estimand involved substituting a single exposure with a single component, provided all variables were measured in the same units. Bias occurred when the substitution involved >1 substituting component. Leave-one-out models that examined foods in mass while adjusting for total energy intake evaluated obscure estimands.

Conclusions

Regardless of the approach, substitution models need to be constructed from clearly defined causal effect estimands. Estimands involving a single exposure and a single substituting component are typically estimated more accurately than estimands involving more complex substitutions. The practice of examining foods measured in grams or portions while adjusting for total energy intake is likely to deliver obscure relative effect estimands with unclear interpretations.

SUBMITTER: Tomova GD

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

REPOSITORIES: biostudies-literature

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Publications

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

Tomova Georgia D GD Gilthorpe Mark S MS Tennant Peter Wg PW

The American journal of clinical nutrition 20221101 5

<h4>Background</h4>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.<h4>Objectives</h4>We aimed to explore and illustrate the theory and performance of the leave-one-out and energy partitio ...[more]

PMID: 36223891

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Dataset Information

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

Background

Objectives

Methods

Results

Conclusions

Publications

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

Similar Datasets

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