Detecting treatment effects with combinations of the ADAS-cog items in patients with mild and moderate Alzheimer's disease.

OBJECTIVE: When complex cognitive functions are measured with multi-item scales like the Alzheimer's Disease Assessment Scale - cognitive subscale (ADAS-cog), it seems valuable information can be lost due to combination of the ADAS-cog items results into a total score. We hypothesized, that an analysis of the results of different ADAS-cog item combinations may reveal drug treatment effects in distinct cognitive domains and/or enhance the sensitivity to detect such treatment effects. Here, we present a novel approach called 'subsetting analysis' for assessment of drug treatment effects with multi-item scales, like the ADAS-cog. METHODS: The subsetting approach is a mathematical algorithm designed to select and group scale items in a subset detecting drug treatment effects in a particular study population. The approach was applied in a post-hoc analysis of ADAS-cog results from two randomized, placebo-controlled and double-blind clinical trials with memantine in mild to moderate Alzheimer's disease (AD). The subsetting analysis of the ADAS-cog combined database aimed at selecting the scale items showing no worsening at study end compared to baseline due to memantine treatment in mild AD (Mini-Mental State Examination (MMSE >19)) patients. RESULTS: Two ADAS-cog subsets were finally revealed by the analysis: a subset of five ADAS-cog items, identified as most sensitive to memantine effects in mild AD patients, and a subset of six ADAS-cog items shown to detect significant memantine effects in moderate AD patients. CONCLUSION: The subsetting approach of analyzing ADAS-cog data is a powerful alternative for gaining information about drug effects on cognitive performance in mild and moderate AD patients.