Williamson G D, Haber M
Epidemiology Program Office, Centers for Disease Control and Prevention, Atlanta, Georgia 30333.
Biometrics. 1994 Mar;50(1):194-203.
We develop models for three-dimensional contingency tables containing both completely and partially cross-classified data for which one of the variables is regarded as dependent and the other two variables are regarded as independent variables. Parameters of interest include the cell probabilities and the probabilities that the observations on one or both independent variables are missing. The models allow inferences on these two sets of probabilities to be made independently. Maximum likelihood methods for estimating and testing hypotheses regarding these parameters are described, along with conditional goodness-of-fit test statistics, which display a convenient additivity property. The methodology is applied to cervical cancer data from a case-control study performed in Atlanta, Georgia, 1985-1988.
我们针对三维列联表开发模型，该表包含完全交叉分类和部分交叉分类的数据，其中一个变量被视为因变量，另外两个变量被视为自变量。感兴趣的参数包括单元格概率以及一个或两个自变量的观测值缺失的概率。这些模型允许对这两组概率进行独立推断。描述了用于估计和检验关于这些参数的假设的最大似然方法，以及具有便利可加性的条件拟合优度检验统计量。该方法应用于1985 - 1988年在佐治亚州亚特兰大进行的一项病例对照研究中的宫颈癌数据。
