Journal of Data Science

Abstract: When there is a rare disease in a population, it is inefficient to take a random sample to estimate a parameter. Instead one takes a random sample of all nuclear families with the disease by ascertaining at least one affected sibling (proband) of each family. In these studies, an estimate of the proportion of siblings with the disease will be inflated. For example, studies of the issue of whether a rare disease shows an autosomal recessive pattern of inheritance, where the Mendelian segregation ratios are of interest, have been investigated for several decades. How do we correct for this ascertainment bias? Methods, primarily based on maximum likelihood estimation, are available to correct for the ascertainment bias. We show that for ascertainment bias, although maximum likelihood estimation is optimal under asymptotic theory, it can perform badly. The problem is exasperated in the situation where the proband probabilities are allowed to vary with the number of affected siblings. We use two data sets to illustrate the difficulties of maximum likelihood estimation procedure, and we use a simulation study to assess the quality of the maximum likelihood estimators.