De Vent et al. (2016). Advanced neuropsychological diagnostics infrastructure (ANDI): A normative database created from control datasets.” - Article summary

In the advanced neuropsychological diagnostics infrastructure (ANDI), datasets of several research groups are combined into a single database. It contains scores of neuropsychological tests from healthy participants. This allows for accurate neuropsychological assessment as the quantity and the range of the data surpasses most traditional normative data. It facilitates normative comparison methods (e.g. those in which entire profiles of scores are evaluated).

An important element of neuropsychological practice is to determine whether a patient who presents with cognitive complaints has abnormal scores on neuropsychological tests. In the diagnostic process, a number of neuropsychological tests are administered and the test results of a patient are compared to a normative sample.

Scores of patients are typically compared to normative data published in the manuals of an instrument. However, this data may be outdated (1), it may lack norms for very old populations (2), some tests do not have any norms (3), normative scores are often only corrected for age but not for other demographic variables (4) and normative data are often collected for one test at a time (5). This results in univariate but not multivariate data being available while multivariate normative comparison methods are more sensitive to deviating profiles of test scores.

There are several benefits of the ANDI database:

1. More appropriate norms
   The ANDI database may provide more appropriate norms because the data has been collected over a long period of time and are easily updated (i.e. internet-based database) (1), there is a lot of data on older participants (2), the data comes from representative participants in different countries (3), the scores are corrected for demographic variables (e.g. sex) (4), age is treated as a continuous, rather than arbitrary discrete variable (i.e. age groups) (5) and the norms are based on a large sample (6).
2. Multivariate data
   The ANDI database consists of multivariate data as many participants completed multiple tests. This allows for multivariate comparisons which have increased sensitivity to detect cognitive impairment.
3. Exportable infrastructure
   The software of the ANDI database is freely available for researchers to do their own research. This allows them to add to the existing ANDI database or create their own.

Limitations of the ANDI database are that it is not necessarily based on a random sample (1) and some participants had lenient inclusion criteria and, thus, did not necessarily have no pathology (2).