Improving multilevel analyses: the integrated epidemiologic design.

ABSTRACT

Multilevel analysis has been widely used to allow the simultaneous examination of the effects of individual- and group-level variables on individual health outcomes. In spite of its utility, multilevel design can have some drawbacks in the estimation of risk factor effects when the within-group variation of variables of interest is small relative to between-group variation. An extreme case of this is a group-level risk factor, which by definition has no within-group variation. To improve the estimation of group-level and individual-level risk factor effects, we consider an integrated epidemiologic design using a population-based estimating equation approach that can be considered a further extension of the multilevel design. Although the integrated design uses the same individual-level and group-level data as the multilevel design, it includes aggregated health outcome data in each group as additional information. This paper explains differences between the 2 designs, describing advantages and disadvantages of the integrated design over the multilevel design. The 2 designs are applied to a real example of mortality following chronic kidney disease, illustrating differences that might be encountered in practice.