title: Statistical evaluation of age-at-onset anticipation: a new test and evaluation of its behavior in realistic applications.
abstract: The discovery that microsatellite repeat expansions can cause clinical disease has fostered renewed interest in testing for age-at-onset anticipation (AOA). A commonly used procedure is to sample affected parent-child pairs (APCPs) from available data sets and to test for a difference in mean age at onset between the parents and the children. However, standard statistical methods fail to take into account the right truncation of both the parent and child age-at-onset distributions under this design, with the result that type I error rates can be inflated substantially. Previously, we had introduced a new test, based on the correct, bivariate right-truncated, age-at-onset distribution. We showed that this test has the correct type I error rate for random APCPs, even for quite small samples. However, in that paper, we did not consider two key statistical complications that arise when the test is applied to realistic data. First, affected pairs usually are sampled from pedigrees preferentially selected for the presence of multiple affected individuals. In this paper, we show that this will tend to inflate the type I error rate of the test. Second, we consider the appropriate probability model under the alternative hypothesis of true AOA due to an expanding microsatellite mechanism, and we show that there is good reason to believe that the power to detect AOA may be quite small, even for substantial effect sizes. When the type I error rate of the test is high relative to the power, interpretation of test results becomes problematic. We conclude that, in many applications, AOA tests based on APCPs may not yield meaningful results.
authors.affiliation: Department of Preventive Medicine, College of Medicine, University of Iowa, Iowa City, IA 52242, USA. veronica-vieland@uiowa.edu
authors.forename: V J
authors.initials: VJ
authors.lastname: Vieland
authors.forename: J
authors.initials: J
authors.lastname: Huang
mesh_terms: D000367:Age Factors; D005069:Evaluation Studies as Topic; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical