文章基本信息

标题：Improving estimation in genetic models using prior information
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作者：Espen Moen Eilertsen
期刊名称：Norsk epidemiologi
印刷版ISSN：0803-2491
出版年度：2016
卷号：26
期号：1-2
DOI：10.5324/nje.v26i1-2.2017
语种：English
出版社：Norsk forening for epidemiologi - The Norwegian Epidemiological Association
摘要：Statistical models used to investigate research questions in behavioral genetics often require large amountsof data. This paper introduces some key concepts of Bayesian analysis and illustrates how these methodscan aid model estimation when the data does not provide enough information to reliably answer researchquestions. The use of informative prior distributions is discussed as a method of incorporating informationfrom other sources than the data at hand. The procedure is illustrated with an ACE model decomposition ofthe variance of antisocial personality disorder. The data originates from the Norwegian Twin Registry, andincludes adult twins assessed with the Structured Interview for DSM Personality (SIDP-IV). Inclusion ofprior information lead to a shift with respect to conclusions about the presence of shared environmentaleffects compared to a traditional analysis. Small and medium sized studies should consider use of priorinformation to aid estimation of population parameters.
其他摘要：Statistical models used to investigate research questions in behavioral genetics often require large amounts of data. This paper introduces some key concepts of Bayesian analysis and illustrates how these methods can aid model estimation when the data does not provide enough information to reliably answer research questions. The use of informative prior distributions is discussed as a method of incorporating information from other sources than the data at hand. The procedure is illustrated with an ACE model decomposition of the variance of antisocial personality disorder. The data originates from the Norwegian Twin Registry, and includes adult twins assessed with the Structured Interview for DSM Personality (SIDP-IV). Inclusion of prior information lead to a shift with respect to conclusions about the presence of shared environmental effects compared to a traditional analysis. Small and medium sized studies should consider use of prior information to aid estimation of population parameters.