Linear Measurement Error Models with Restricted Sampling

• Published in: Biometrics Vol. 63; no. 1; pp. 137 - 142
• Main Authors: Gorfine, Malka, Lipshtat, Nurit, Freedman, Laurence S, Prentice, Ross L
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.03.2007; International Biometric Society; Blackwell Publishing Ltd
• Subjects: Analytical estimating; Biometrics; biometry; Chronic Disease; chronic diseases; Diet; Diet Records; energy intake; epidemiological studies; Epidemiology; Error rates; Estimation bias; Estimation methods; Feeding Behavior; food frequency questionnaires; food intake; food records; Humans; linear models; Measurement error; Missing data; Modeling; Models, Statistical; Monte Carlo Method; Multiple imputation for longitudinal data; Nutrient intake; nutrients; Nutritional epidemiology; nutritional intervention; Regression analysis; Research methodology; Restricted sample; risk; Sample Size; Sampling bias; Statistical analysis; Statistical discrepancies; Surveys and Questionnaires
• ISSN: 0006-341X; 1541-0420
• DOI: 10.1111/j.1541-0420.2006.00624.x

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.