[The problem of small "n" and big "P" in neuropsycho-pharmacology, or how to keep the rate of false discoveries under control].
One of the characteristics of many methods used in neuropsychopharmacology is that a large number of parameters (P) are measured in relatively few subjects (n). Functional magnetic resonance imaging, electroencephalography (EEG) and genomic studies are typical examples. For example one microarray chip can contain thousands of probes. Therefore, in studies using microarray chips, P may be several thousand-fold larger than n. Statistical analysis of such studies is a challenging task and they are refereed to in the statistical literature such as the small "n" big "P" problem. The problem has many facets including the controversies associated with multiple hypothesis testing. A typical scenario in this context is, when two or more groups are compared by the individual attributes. If the increased classification error due to the multiple testing is neglected, then several highly significant differences will be discovered. But in reality, some of these significant differences are coincidental, not reproducible findings. Several methods were proposed to solve this problem. In this review we discuss two of the proposed solutions, algorithms to compare sets and statistical hypothesis tests controlling the false discovery rate.
Semmelweis Egyetem, Gyógyszerhatástani Intézet, Budapest, Hungary. bagdy.gyorgy@pharma.semmelweis-univ.hu.
Péter
P
Petschner
György
G
Bagdy
Laszló
L
Tóthfalusi
D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D020869:Gene Expression Profiling; D006801:Humans; D008279:Magnetic Resonance Imaging; D046228:Microarray Analysis; D058027:Neuropsychiatry / Q000592:standards / Q000639:trends; D011600:Psychopharmacology / Q000592:standards / Q000639:trends; D012107:Research Design / Q000592:standards / Q000639:trends; D018401:Sample Size