Quasi-Monte Carlo Methods for Binary Event Models with Complex Family Data
针对家庭数据中二元结果的广义线性混合模型，提出一种结合梯度与海森矩阵近似的拟蒙特卡罗方法，大幅提升似然计算速度，并用于瑞典强迫症多代数据研究。
The generalized linear mixed model for binary outcomes with the probit link function is used in many fields but has a computationally challenging likelihood when there are many random effects. We extend a previously used importance sampler, making it much faster in the context of estimating heritability and related effects from family data by adding a gradient and a Hessian approximation and making a faster implementation. Additionally, a graph-based method is suggested to simplify the likelihood when there are thousands of individuals in each family. Simulation studies show that the resulting method is orders of magnitude faster, has a negligible efficiency loss, and confidence intervals with nominal coverage. We also analyze data from a large study of obsessive-compulsive disorder based on Swedish multi-generational data. In this analysis, the proposed method yielded similar results to a previous analysis, but was much faster. Supplementary materials for this article are available online.