Once the review of the data definitions and values described above indicates that data from different studies are comparable, common definitions and values need to be agreed upon. WGs may combine categories into larger units (e.g. drinks per day combined with drinking days per week to get drinks per week), stipulate inclusion and exclusion criteria (e.g. excluding those who have never had a drink), create a dichotomous variable (e.g. ever or never drinker, or use longitudinal and cross-sectional data to define whether the respondent is a current smoker or has quit smoking), or assign a standard measure to be used (e.g. BMI calculated from a variety of self-reported or laboratory assessments of height and weight). In general, the more tightly defined the phenotype, the greater the likelihood that one or more studies may be unable to contribute to the analyses; the looser the definition, the greater the likelihood that more subjects and more studies can be included. An example of how data from various studies might be combined is shown in Tables 2a and 2b.
