Geriatric Psychiatry Centre, Alexian Hospital Maria-Hilf, Krefeld and Department of Psychiatry and Psychotherapy, University of Duesseldorf, Germany.
Int J Geriatr Psychiatry. 2012 Jan;27(1):15-21. doi: 10.1002/gps.2679. Epub 2011 Mar 8.
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.
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.
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.
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.
当使用多项目量表（如阿尔茨海默病评估量表 - 认知子量表（ADAS-cog））测量复杂的认知功能时，由于将 ADAS-cog 项目的结果组合成总分，似乎会丢失有价值的信息。我们假设，对不同 ADAS-cog 项目组合结果的分析可能会揭示药物治疗在不同认知领域的效果，或者提高检测这些治疗效果的敏感性。在这里，我们提出了一种新的方法，称为“子集分析”，用于评估多项目量表（如 ADAS-cog）的药物治疗效果。
子集分析方法是一种数学算法，旨在选择和组合子集中的量表项目，以检测特定研究人群中的药物治疗效果。该方法应用于两项随机、安慰剂对照和双盲临床试验的 ADAS-cog 结果的事后分析，该试验使用美金刚治疗轻度至中度阿尔茨海默病（AD）。ADAS-cog 联合数据库的子集分析旨在选择与基线相比，由于美金刚治疗在轻度 AD（Mini-Mental State Examination（MMSE>19）患者中没有恶化的量表项目。
最终通过分析揭示了两个 ADAS-cog 子集：一个包含五个 ADAS-cog 项目的子集，被确定为对轻度 AD 患者的美金刚效果最敏感；另一个包含六个 ADAS-cog 项目的子集，被证明可以检测到中度 AD 患者中美金刚的显著效果。
分析 ADAS-cog 数据的子集分析方法是一种获取关于轻度和中度 AD 患者认知功能药物效应信息的有力替代方法。
