30,000 - this is the number of genes that can be analysed simultaneously with state-of-the-art instruments. Such analyses provide a means of identifying whether individual genes have a decisive impact in the course of a disease or therapy. However, the more genes are examined in a study, the greater the probability of incorrectly identifying a gene as a factor when, in reality, it has no influence.
Dr. Sonja Zehetmayer from the Department of Medical Statistics, Medical University of Vienna, says: "The problem of identifying factors incorrectly could be countered by a very high number of repetition. However, repetition normally needs to be kept to a minimum, owing to high costs. A more innovative approach to solve this problem is offered by multi-stage methods. These involve preselecting genes following the first examination stage. In subsequent stages, only these selected genes are subject to further analysis. Concentrating on fewer genes cuts error probability."
Researchers have now found how many stages are needed to deliver an optimal cost-benefit ratio: The solution turned out to be unexpectedly straightforward - three stages deliver the optimal ratio between the accuracy of the results obtained and the costs necessary for this. Although a fourth stage would offer greater accuracy, the resources this would require are out of all proportion to the additional accuracy gained.
Zehetmayer also found surprising results when she compared two different test designs with each other: "Multi-stage series of tests can be analysed either by integrating the results of all levels or by analysing the results of only the last stage. While the choice of test design for four-stage methods has a marked effect on its statistical characteristics, this effect is mitigated in the case of a three-stage method."
MEDICA.de; Source: Austrian Science Fund FWF