Research in Dietary Quality Data Analysis

Eunice Kennedy Shriver National Institute Of Child Health & Human Development

Investigators

Abstract

Dietary interventions often target foods that are under consumed relative to dietary guidelines, such as vegetables, fruits, and whole grains. Because these foods are only consumed episodically for some participants, data from such a study often contains a disproportionally large number of zeros due to study participants who do not consume any of the target foods on the days that dietary intake is assessed, thus generating semicontinuous data. These zeros need to be properly accounted for when calculating sample sizes to ensure that the study is adequately powered to detect a meaningful intervention effect size. Nonetheless, this issue has not been well addressed in the literature. Instead, methods that are common for continuous outcomes are typically used to compute the sample sizes, resulting in a substantially under- or overpowered study. we proposed proper approaches to calculating the sample size needed for dietary intervention studies that target episodically consumed foods. They derived sample size formulae are for detecting the mean difference in the amount of intake of an episodically consumed food between an intervention and a control group. Their numerical studies and simulation results show that the proposed formulae are appropriate for estimating the sample sizes needed to achieve the desired power for the study. The proposed method for sample size is expected to be utilized for designing future dietary intervention studies targeting episodically consumed foods. We also proposed a Bernoulli-normal mixture model for clustering of multivariate semicontinuous data and demonstrated its accuracy as compared to the well-known clustering method with the conventional normal mixture model. The proposed method was applied to the baseline dietary date from the CHEF study. Baseline foods consumptions data in the trial are used to explore preintervention dietary patterns among study participants. While the conventional normal mixture model approach fails to do so, the proposed Bernoulli-normal mixture model approach has shown to be able to identify a dietary profile that significantly differentiates the intervention effects from others, as measured by the popular healthy eating index at the end of the trial. The findings are potentially useful in that future intervention can target patients with certain dietary patterns.