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The cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-Cog) is used as an efficacy measure in clinical drug trials of Alzheimer's disease (AD). We used data from 1,648 AD participants in two identical 26-week multicenter drug trials to examine the distribution of baseline ADAS-Cog scores in relation to selected demographic and clinical variables, Mini-Mental State Exam (MMSE), Global Deterioration Scale (GDS), and Geriatric Evaluation by Relative's Rating Instrument (GERRI) scores. At baseline, the mean (+/-SD) MMSE score was 18 +/- 4, the ADAS-Cog score was 28 +/- 11, and most subjects were in GDS stage 4 or 5. The ADAS-Cog score was statistically significantly correlated with MMSE (R = -0.76, p < 0.0001) and GERRI (R = 0.40, p < 0.0001) total scores. Correlations among the ADAS-Cog items ranged from 0.19 to 0.59 and all were statistically significant (p < 0.0001). In a multiple regression model, younger age, male gender, older age at onset of dementia, use of concurrent estrogen, and use of concurrent anti-inflammatory agents were statistically significantly associated with superior cognitive performance. We also present data on the distribution of ADAS-Cog scores in relation to subjects' age, level of education, MMSE score, and GDS stage. Because age, MMSE score, and GDS stage (and not the ADAS-Cog) are commonly used to select subjects for AD clinical trials, our data should improve the ability of sponsors to predict ADAS-Cog scores of the subjects in their trials on the basis of the inclusion criteria used. Our data also suggest that age, gender, age at onset of dementia, level of education, and use of estrogen (in women) or anti-inflammatory drugs are related to cognitive abilities in AD. Further studies are needed to assess how and when cognitive differences related to these variables arise.

With increasing numbers of treatment options available for patients with major depression over the last decade and the growing body of evidence describing their efficacy and safety, clinicians often find it difficult to determine the best and most appropriate evidence-based treatment for each patient. Systematic reviews utilizing statistical methods that synthesize and evaluate data from a number of studies have become increasingly more available over the past decade. We review major findings and lessons learned from salient examples of quantitative analyses of antidepressant research and provide recommendations for meta-analysts, journal and grant reviewers, and research 'consumers' (ie, clinicians) for conducting, reporting, and evaluating such analyses.