As AUDIT-C and AUDIT-P are computed using an unweighted composite score approach, they inherently rely on the assumptions that (i) the scale is unidimensional, and (ii) each item is equally informative of the construct being measured. This approach is not based on any empirical evidence but rather reflects a holdover from the original use of the AUDIT as a screener for primary health care settings. Therefore, it is possible that the lack of item-specific weights introduces error in downstream analyses. While these issues have been thoroughly studied at the phenotypic level via factor analysis (Table S1), they have not yet been investigated at the genetic level. Using methods that can account for, or mitigate, such measurement problems will allow researchers to better capitalize on the potential of dimensional measures like AUDIT for genetic discovery.
