Statistical issues related to dietary intake as the response variable in intervention trials
Bibliographic record
Abstract
The focus of this paper is dietary intervention trials. We explore the statistical issues involved when the response variable, intake of a food or nutrient, is based on self-report data that are subject to inherent measurement error. There has been little work on handling error in this context. A particular feature of self-reported dietary intake data is that the error may be differential by intervention group. Measurement error methods require information on the nature of the errors in the self-report data. We assume that there is a calibration sub-study in which unbiased biomarker data are available. We outline methods for handling measurement error in this setting and use theory and simulations to investigate how self-report and biomarker data may be combined to estimate the intervention effect. Methods are illustrated using data from the Trial of Nonpharmacologic Intervention in the Elderly, in which the intervention was a sodium-lowering diet and the response was sodium intake. Simulations are used to investigate the methods under differential error, differing reliability of self-reports relative to biomarkers and different proportions of individuals in the calibration sub-study. When the reliability of self-report measurements is comparable with that of the biomarker, it is advantageous to use the self-report data in addition to the biomarker to estimate the intervention effect. If, however, the reliability of the self-report data is low compared with that in the biomarker, then, there is little to be gained by using the self-report data. Our findings have important implications for the design of dietary intervention trials. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.696 | 0.895 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".