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Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study.

ABSTRACT: In lifestyle intervention trials, where the goal is to change a participant's weight or modify their eating behavior, self-reported diet is a longitudinal outcome variable that is subject to measurement error. We propose a statistical framework for correcting for measurement error in longitudinal self-reported dietary data by combining intervention data with auxiliary data from an external biomarker validation study where both self-reported and recovery biomarkers of dietary intake are available. In this setting, dietary intake measured without error in the intervention trial is missing data and multiple imputation is used to fill in the missing measurements. Since most validation studies are cross-sectional, they do not contain information on whether the nature of the measurement error changes over time or differs between treatment and control groups. We use sensitivity analyses to address the influence of these unverifiable assumptions involving the measurement error process and how they affect inferences regarding the effect of treatment. We apply our methods to self-reported sodium intake from the PREMIER study, a multi-component lifestyle intervention trial.

SUBMITTER: Siddique J

PROVIDER: S-EPMC7593985 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study.

Siddique Juned J Daniels Michael J MJ Carroll Raymond J RJ Raghunathan Trivellore E TE Stuart Elizabeth A EA Freedman Laurence S LS

Biometrics 20190406 3

In lifestyle intervention trials, where the goal is to change a participant's weight or modify their eating behavior, self-reported diet is a longitudinal outcome variable that is subject to measurement error. We propose a statistical framework for correcting for measurement error in longitudinal self-reported dietary data by combining intervention data with auxiliary data from an external biomarker validation study where both self-reported and recovery biomarkers of dietary intake are available ...[more]

PMID: 30724332

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Project description:BackgroundLittle evidence is available on searches for non-randomized studies (NRS) in bibliographic databases within the framework of systematic reviews. For instance, it is currently unclear whether, when searching for NRS, effective restriction of the search strategy to certain study types is possible. The following challenges need to be considered: 1) For non-randomized controlled trials (NRCTs): whether they can be identified by established filters for randomized controlled trials (RCTs). 2) For other NRS types (such as cohort studies): whether study filters exist for each study type and, if so, which performance measures they have. The aims of the present analysis were to identify and validate existing NRS filters in MEDLINE as well as to evaluate established RCT filters using a set of MEDLINE citations.MethodsOur analysis is a retrospective analysis of study filters based on MEDLINE citations of NRS from Cochrane reviews. In a first step we identified existing NRS filters. For the generation of the reference set, we screened Cochrane reviews evaluating NRS, which covered a broad range of study types. The citations of the studies included in the Cochrane reviews were identified via the reviews' bibliographies and the corresponding PubMed identification numbers (PMIDs) were extracted from PubMed. Random samples comprising up to 200 citations (i.e. 200 PMIDs) each were created for each study type to generate the test sets.ResultsA total of 271 Cochrane reviews from 41 different Cochrane groups were eligible for data extraction. We identified 14 NRS filters published since 2001. The study filters generated between 660,000 and 9.5 million hits in MEDLINE. Most filters covered several study types. The reference set included 2890 publications classified as NRS for the generation of the test sets. Twelve test sets were generated (one for each study type), of which 8 included 200 citations each. None of the study filters achieved sufficient sensitivity (≥ 92%) for all of the study types targeted.ConclusionsThe performance of current NRS filters is insufficient for effective use in daily practice. It is therefore necessary to develop new strategies (e.g. new NRS filters in combination with other search techniques). The challenges related to NRS should be taken into account.

Dataset Information

Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study.

Publications

Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study.

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