Linear measurement error models with restricted sampling

Malka Gorfine, Nurit Lipshtat, Laurence S. Freedman, Ross L. Prentice

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The relationship between nutrient consumption and chronic disease risk is the focus of a large number of epidemiological studies where food frequency questionnaires (FFQ) and food records are commonly used to assess dietary intake. However, these self-assessment tools are known to involve substantial random error for most nutrients, and probably important systematic error as well. Study subject selection in dietary intervention studies is sometimes conducted in two stages. At the first stage, FFQ-measured dietary intakes are observed and at the second stage another instrument, such as a 4-day food record, is administered only to participants who have fulfilled a prespecified criterion that is based on the baseline FFQ-measured dietary intake (e.g., only those reporting percent energy intake from fat above a prespecified quantity). Performing analysis without adjusting for this truncated sample design and for the measurement error in the nutrient consumption assessments will usually provide biased estimates for the population parameters. In this work we provide a general statistical analysis technique for such data with the classical additive measurement error that corrects for the two sources of bias. The proposed technique is based on multiple imputation for longitudinal data. Results of a simulation study along with a sensitivity analysis are presented, showing the performance of the proposed method under a simple linear regression model.

Original languageEnglish
Pages (from-to)137-142
Number of pages6
JournalBiometrics
Volume63
Issue number1
DOIs
StatePublished - Mar 2007

Funding

FundersFunder number
National Cancer InstituteP01CA053996

    Keywords

    • Measurement error
    • Missing data
    • Multiple imputation for longitudinal data
    • Nutritional epidemiology
    • Restricted sample

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