Application of Bayesian inference using Gibbs sampling to item-response theory modeling of multi-symptom genetic data
Application of Bayesian inference using Gibbs sampling to item-response theory modeling of multi-symptom genetic data
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DOI：
10.1007/s10519-005-7284-z
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发表时间：
2005-11-01
影响因子：
2.6
通讯作者：
Foley, D
中科院分区：
文献类型：
--
作者：
Eaves, L;Erkanli, A;Foley, D
用与青春期抑郁症有关的33个项目的情绪和感觉问卷对1086名青春期女性双生子的反应进行了项目反应理论（IRT）模型拟合。马尔可夫链蒙特卡罗（MCMC）算法被用于贝叶斯框架内的推理使用吉布斯采样，在程序WinBUGS 1.4中实现。最终的模型结合了单独的遗传和非共享的环境特征（“A和E”）和项目特定的遗传效应。较简单的模型得到了明显较差的拟合偏差信息准则（DIC）判断的意见。共同的遗传因素显示出对自我贬低项目的主要负荷，而环境因素则在与自我贬低相关的项目上负荷最高。MCMC方法提供了一个方便和灵活的替代最大似然估计IRT模型的参数相对较大数量的项目在遗传背景下。IRT方法的其他好处进行了讨论，包括潜在的特质分数，包括遗传因素分数，和他们的抽样误差的估计。
Several "genetic" item-response theory (IRT) models are fitted to the responses of 1086 adolescent female twins to the 33 multi-category item Mood and Feeling Questionnaire relating to depressive symptomatology in adolescence. A Markov-chain Monte Carlo (MCMC) algorithm is used within a Bayesian framework for inference using Gibbs sampling, implemented in the program WinBUGS 1.4. The final model incorporated separate genetic and non-shared environmental traits ("A and E") and item-specific genetic effects. Simpler models gave markedly poorer fit to the observations judged by the deviance information criterion (DIC). The common genetic factor showed major loadings on melancholic items, while the environmental factor loaded most highly on items relating to self-deprecation. The MCMC approach provides a convenient and flexible alternative to Maximum Likelihood for estimating the parameters of IRT models for relatively large numbers of items in a genetic context. Additional benefits of the IRT approach are discussed including the estimation of latent trait scores, including genetic factor scores, and their sampling errors.