Creating Composite Indices From Continuous Variables for Research: The Geometric Mean.

Clinical research focuses on the relationship between one or more independent variables and some dependent variable or outcome chosen to reflect some underlying process. For categorical variables, the research may either be focused on a specific end point such as myocardial infarction (MI) or on an underlying construct such as vascular disease (with MI as just one exemplar). In the latter instance, a composite index such as major adverse cardiovascular event (MACE), defined as either a nonfatal stroke, nonfatal MI, or cardiovascular death, may be used. Composite categorical outcomes such as MACE optimize power by ensuring a high event rate, and the results they yield are generalizable to diseases that are consistent with the underlying construct. It is therefore surprising that there is no widely used method to combine continuous variables into composite continuous outcomes. Nevertheless, there is a clear need for such a methodology when the underlying construct cannot be easily captured by one measurement. Glucose control is an example of a construct that can be assessed in many ways, including fasting or postprandial plasma glucose, HbA1c, fructosamine, or “time in target.” A composite of two or more of these could provide a better reflection of glucose control than any one alone. Whereas sophisticated statistical techniques such as structural equation modeling (1) can be used to model some underlying construct or latent variable from two or more measurements, a simpler way of combining them into an index that reflects the underlying construct could provide a powerful tool for both researchers and clinicians. Such an approach is described below. When the same measurements are made using the …