Chen Yuguo, Dinwoodie Ian H, MacGibbon Brenda
Department of Statistics, University of Illinois at Urbana-Champaign, 725 S. Wright Street, Champaign, Illinois 61820, USA.
Biometrics. 2007 Sep;63(3):845-55. doi: 10.1111/j.1541-0420.2007.00763.x.
The problem of exact conditional inference for discrete multivariate case-control data has two forms. The first is grouped case-control data, where Monte Carlo computations can be done using the importance sampling method of Booth and Butler (1999, Biometrika86, 321-332), or a proposed alternative sequential importance sampling method. The second form is matched case-control data. For this analysis we propose a new exact sampling method based on the conditional-Poisson distribution for conditional testing with one binary and one integral ordered covariate. This method makes computations on data sets with large numbers of matched sets fast and accurate. We provide detailed derivation of the constraints and conditional distributions for conditional inference on grouped and matched data. The methods are illustrated on several new and old data sets.
离散多变量病例对照数据的精确条件推断问题有两种形式。第一种是分组病例对照数据，可使用Booth和Butler（1999年，《生物统计学》86卷，321 - 332页）的重要性抽样方法或一种提议的替代序贯重要性抽样方法进行蒙特卡罗计算。第二种形式是匹配病例对照数据。对于此分析，我们基于条件泊松分布提出一种新的精确抽样方法，用于对一个二元和一个有序整数协变量进行条件检验。该方法能快速且准确地对具有大量匹配集的数据集进行计算。我们详细推导了分组和匹配数据条件推断的约束条件和条件分布。在几个新旧数据集上对这些方法进行了说明。
