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Missing data in a long food frequency questionnaire: are imputed zeroes correct?
Gary E Fraser1, Ru Yan, Terry L Butler
1Department of Epidemiology and Biostatistics, Loma Linda University, Loma Linda, CA, USA. gfraser@llu.edu
Epidemiology (Cambridge, Mass.)
|January 30, 2009
Summary
Missing dietary data are common and often not random. Automatically imputing zero values for missing data can be inaccurate, especially for frequently consumed foods, requiring careful analysis for specific subgroups.
Area of Science:
- Nutritional epidemiology
- Dietary assessment
- Biostatistics
Background:
- Missing data present a significant challenge in nutritional epidemiology.
- Understanding the characteristics of missing dietary data is crucial for accurate imputation.
Purpose of the Study:
- To characterize missing data in the Adventist Health Study-2 cohort.
- To evaluate the impact of zero imputation for missing dietary variables.
Main Methods:
- A random sample of 20% of subjects (n=2091) with missing dietary variables were telephoned.
- Responses were obtained for 92% of the missing variables.
Main Results:
- Missing data showed an excess of "zero" intakes, but non-zero missing data were common for frequently consumed foods.
- Older, Black, and less-educated subjects had higher rates of missing data.
- Zero imputation may introduce bias, particularly for frequently consumed items (over 5-10% missing).
Conclusions:
- Missing dietary data are not always true zeroes and are often not missing at random.
- Automatic zero imputation can be incorrect and lead to bias, especially for frequently consumed foods.
- Specific subgroups require careful consideration during data imputation due to higher missing data rates.
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