Pooling dietary data using questionnaires with open-ended and predefined responses: implications for comparing mean intake or estimating odds ratios.

In the current era of diet-gene analyses, large sample sizes are required to uncover the etiology of complex diseases. As such, consortia form and often combine available data. Food frequency questionnaires, which commonly use 2 different types of responses about the frequency of intake (predefined responses and open-ended responses), may be pooled to achieve the desired sample size. The common practice is to categorize open-ended responses into the predefined response categories. A problem arises when the predefined categories are noncontiguous: possible open-ended responses may fall in gaps between the predefined categories. Using simulated data modeled from frequency of intake among 1,664 controls in a lung cancer case-control study at The University of Texas M. D. Anderson Cancer Center (Houston, Texas, 2000-2005), the authors describe the effect of different categories of open-ended responses that fall in between noncontiguous, predefined response sets on estimates of the mean difference in intake and the odds ratios. A significant inflation of false positives appears when comparing mean differences of intake, while the bias in estimating odds ratios may be acceptably small. Therefore, if pooling data cannot be restricted to the same type of response, inferences should focus on odds ratio estimation to minimize bias.