The three methods that we compare are:
The names given to methods (b) and (c) follow those of Krebs-Smith et al [5 (link)].
It is not immediately clear which method would be least biased, and one can construct different numerical examples where each one of the three is the superior method. The methods must therefore be tested with data that (a) are realistic and conform to typically reported values, and (b) come from a population with known population mean usual HEI-2005 component scores. Unfortunately, available real datasets do not satisfy condition (b), so we employ instead computer simulations of data generated from a statistical model that is based on real data.
The dataset we used as a basis for our statistical model is drawn from the Eating at America’s Table Study (EATS) [6 (link)]. The study was approved by the National Cancer Institute Special Studies Institutional Review Board. The 738 women we studied were part of a nationally representative sample. Participants were asked to complete four 24HRs via telephone over a period of one year (1997–98), with one recall per season. Six hundred and fifty (88%) of these women completed all four recalls. Foods reported on the 24-hour recalls were coded using the Food Intake Analysis System, version 2., which calculated total daily intakes for energy, saturated fat and sodium. The food codes, in turn, were linked to the MyPyramid Equivalents Database, version 1.0, in order to calculate total daily intakes of the food groups of interest.
Summary statistics on the first day’s reported intake of the 12 HEI-2005 components (and energy) were computed (Table 1 ). Note that the mean ratio is different from the population ratio (final two columns of Table 1 ). In most cases, the mean ratio has the larger value; but for Oils, Saturated Fat, and Solid Fats, Alcoholic beverages, and Added Sugars (SoFAAS), it has the smaller value.
The statistical model forming the basis of our computer simulations was constructed under a set of assumptions and calculations. All of the model parameters were estimated from the data on the women participating in EATS. The details of the estimation procedures are contained in on-lineAppendix A . A brief description of how the model was formed is given below.
Some food groups are not consumed every day by all individuals. We refer to days on which a given food group is consumed by a given individual as that individual’s “consumption days,” the remaining days being the individual’s “non-consumption days.”
First we made an assumption about the intake distributions. Distributions of intake on consumption days, both between individuals and within individuals, were assumed to be normal after a suitable power transformation. The power transformation for each food/nutrient was individually chosen after inspection of the deciles of the distribution (see second column of on-lineSupplemental Table 1 ).
For food groups (but not for nutrients), there is a probability of non-consumption on a single day. We examined three assumptions regarding this probability, each of increasing complexity.
The names given to methods (b) and (c) follow those of Krebs-Smith et al [5 (link)].
It is not immediately clear which method would be least biased, and one can construct different numerical examples where each one of the three is the superior method. The methods must therefore be tested with data that (a) are realistic and conform to typically reported values, and (b) come from a population with known population mean usual HEI-2005 component scores. Unfortunately, available real datasets do not satisfy condition (b), so we employ instead computer simulations of data generated from a statistical model that is based on real data.
The dataset we used as a basis for our statistical model is drawn from the Eating at America’s Table Study (EATS) [6 (link)]. The study was approved by the National Cancer Institute Special Studies Institutional Review Board. The 738 women we studied were part of a nationally representative sample. Participants were asked to complete four 24HRs via telephone over a period of one year (1997–98), with one recall per season. Six hundred and fifty (88%) of these women completed all four recalls. Foods reported on the 24-hour recalls were coded using the Food Intake Analysis System, version 2., which calculated total daily intakes for energy, saturated fat and sodium. The food codes, in turn, were linked to the MyPyramid Equivalents Database, version 1.0, in order to calculate total daily intakes of the food groups of interest.
Summary statistics on the first day’s reported intake of the 12 HEI-2005 components (and energy) were computed (
The statistical model forming the basis of our computer simulations was constructed under a set of assumptions and calculations. All of the model parameters were estimated from the data on the women participating in EATS. The details of the estimation procedures are contained in on-line
Some food groups are not consumed every day by all individuals. We refer to days on which a given food group is consumed by a given individual as that individual’s “consumption days,” the remaining days being the individual’s “non-consumption days.”
First we made an assumption about the intake distributions. Distributions of intake on consumption days, both between individuals and within individuals, were assumed to be normal after a suitable power transformation. The power transformation for each food/nutrient was individually chosen after inspection of the deciles of the distribution (see second column of on-line
For food groups (but not for nutrients), there is a probability of non-consumption on a single day. We examined three assumptions regarding this probability, each of increasing complexity.
