Replies to post #189224 on Anavex Life Sciences Corp (AVXL)

[TempePhil]

Xena, yes that is the paper. A really good study. In particular table 2 has the data anavex cites for IDN (identification).

[nidan7500]

Think of how much better off we will be when the corresponding ERP data are available/used.

The Australian Imaging, Biomarkers, and Lifestyle (AIBL) study is a prospective natural history study of over 1000 adults who are cognitively normal or have either a diagnosis of MCI or mild AD (Ellis et al., 2009). These individuals undergo extensive assessment using psychiatric, neuropsychological, neurological, neuroradiological, and lifestyle measures at 18-month intervals (Ellis et al., 2009; Rowe et al., 2010). The AIBL Rates of Change substudy (hereafter referred to as ROCS) was designed to leverage the care and attention used in recruiting, assessing and characterizing the subjects in AIBL, by taking a subset of each clinical group and assessing them repeatedly at short retest intervals using the CogState battery to determine the extent to which any change in cognitive function could be detected in individuals with different stages of AD over intervals of 1, 2, 3, 6, 12, and 18 months. As the ROCS study is now enrolled fully and complete to the 3-month assessment, these data can be used to examine the acceptability of the tests in healthy adults, and adults with aMCI and AD, as well as to examine the magnitude of differences in performance between these groups. Some clinical trials of putative cognitive enhancers in AD are also conducted over 3 months and these trials generally measure cognitive performance at baseline and then at multiple follow-up assessments (i.e., weeks 4, 8 and 12; Pietrzak et al., 2009; Rogers et al., 1998). Therefore, we also investigated the stability of performance on the battery over 12 weeks in each of these cognitive measures between groups. Data from this prospective study can provide estimates of the expected rate of change in cognitive function over 12 weeks, as well as estimates of associated error (i.e., test–retest reliability and stability of the different outcome measures). Such data can be useful for computing power in clinical trials conducted over the same time interval. Finally, by restricting our analyses to the very short term, the effects of disease-related variability would be minimized, thus allowing direct comparison of any differences in rates of change or stability of performance in the different stages of the disease.