29 research outputs found

    Recurrence Risk of Autism in Siblings and Cousins: A Multinational, Population-Based Study

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    Objective:Familial recurrence risk is an important population-level measure of the combined genetic and shared familial liability of autism spectrumdisorder (ASD). Objectives were to estimate ASD recurrence risk among siblings and cousins by varying degree of relatedness and by sex.Method:This is a population-based cohort study of livebirths from 1998 to 2007 in California, Denmark, Finland, Israel, Sweden and WesternAustralia followed through 2011 to 2015. Subjects were monitored for an ASD diagnosis in their older siblings or cousins (exposure) and for their ASDdiagnosis (outcome). The relative recurrence risk was estimated for different sibling and cousin pairs, for each site separately and combined, and by sex.Results:During follow-up, 29,998 cases of ASD were observed among the 2,551,918 births used to estimate recurrence in ASD and 33,769 cases ofchildhood autism (CA) were observed among the 6,110,942 births used to estimate CA recurrence. Compared with the risk in unaffected families, therewas an 8.4-fold increase in the risk of ASD following an older sibling with ASD and a 17.4-fold increase in the risk of CA following an older sibling withCA. A 2-fold increase in the risk for cousin recurrence was observed for the 2 disorders. There also was a significant difference in sibling ASD recurrencerisk by sex.Conclusion:The present estimates of relative recurrence risks for ASD and CA will assist clinicians and families in understanding autism risk in thecontext of other families in their population. The observed variation by sex underlines the need to deepen the understanding of factors influencing ASD familial risk.</p

    Simultaneous multidimensional unfolding and cluster analysis: An investigation of strategic groups

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    This paper develops a maximum likelihood based methodology for simultaneously performing multidimensional unfolding and cluster analysis on two-way dominance or profile data. This new procedure utilizes mixtures of multivariate conditional normal distributions to estimate a joint space of stimulus coordinates and K ideal points, one for each cluster or group, in a T -dimensional space. The conditional mixture, maximum likelihood methodology is introduced together with an E-M algorithm utilized for parameter estimation. A marketing strategy application is provided with an analysis of PIMS data for a set of firms drawn from the same competitive industry to determine strategic groups, while simultaneously depicting strategy-performance relationships.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47056/1/11002_2004_Article_BF00436033.pd
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