Questioning the Meaning of a Change on the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog): Noncomparable Scores and Item-Specific Effects Over Time

Longitudinal invariance indicates that a construct is measured over time in the same way, and this fundamental scale property is a sine qua non to track change over time using ordinary mean comparisons. The Alzheimer's Disease Assessment Scale-cognitive (ADAS-Cog) and its subscale scores are often used to monitor the progression of Alzheimer's disease, but longitudinal invariance has not been formally evaluated. A configural invariance model was used to evaluate ADAS-Cog data as a three correlated factors structure for two visits over 6 months, and four visits over 2 years (baseline, 6, 12, and 24 months) among 341 participants with Alzheimer's disease. We also attempted to model ADAS-Cog subscales individually, and furthermore added item-specific latent variables. Neither the three-correlated factors ADAS-Cog model, nor its subscales viewed unidimensionally, achieved longitudinal configural invariance under a traditional modeling approach. No subscale achieved scalar invariance when considered unidimensional across 6 months or 2 years of assessment. In models accounting for item-specific effects, configural and metric invariance were achieved for language and memory subscales. Although some of the ADAS-Cog individual items were reliable, comparisons of summed ADAS-Cog scores and subscale scores over time may not be meaningful due to a lack of longitudinal invariance.

Automated summary

Longitudinal invariance, a fundamental scale property, is essential for tracking change over time using mean comparisons. A study evaluating the ADAS-Cog data found that the three correlated factors model and its subscales did not achieve longitudinal configural invariance under traditional modeling. However, in models considering item-specific effects, structural and metric invariance were achieved for the language and memory subscales.