Evidence map›Paper›PMID 32644103›Full record

Trial reportThe American journal of clinical nutrition2020

Best (but oft-forgotten) practices: sample size and power calculation for a dietary intervention trial with episodically consumed foods.

Wei Zhang, Aiyi Liu, Zhiwei Zhang, Tonja Nansel, Susan Halabi

Open access · greenAbstract readClinical Trial
In one paragraph

Trial report in The American journal of clinical nutrition, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 36% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 2 countries.

Wei ZhangAcademy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
Aiyi LiuBiostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA.
Zhiwei ZhangBiostatistics Branch, Division of Cancer Treatment and Diagnostics, National Cancer Institute, NIH, Bethesda, MD, USA.
Tonja NanselSocial and Behavioral Sciences Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA.
Susan HalabiDepartment of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
Eunice Kennedy Shriver National Institute of Child Health and Human Development · USChinese Academy of Sciences · CNDuke University · USNational Cancer Institute · US

Funding

Enhancing Carbohydrate Quality in Diabetes ManagementZIAHD008805 · NICHD · EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT · PI NANSEL, TONJA · 2009 to 2022
$4.4M
6 · The paper itself

Abstract

Dietary interventions often target foods that are underconsumed 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 propose proper approaches to calculating the sample size needed for dietary intervention studies that target episodically consumed foods. Sample size formulae are derived for detecting the mean difference in the amount of intake of an episodically consumed food between an intervention and a control group. Numerical studies are conducted to investigate the accuracy of the sample size formulae as compared with the ad hoc methods. The 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 recommended for designing dietary intervention studies targeting episodically consumed foods.

Indexed as

DietFruitSample SizeVegetablesHumansMonte Carlo MethodResearch Designdietary intervention trialsepisodic consumptionpowersample sizetype I error

Identifiers

PMID32644103
PMCPMC7528564
OpenAlexW3042083518

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.