Logistic Regression for Correlated Binary Data
研究了在保持边际响应概率为逻辑形式的前提下，对相关二元结果进行建模的方法，讨论了不同关联度量，并针对任意数量相关观测提出了伪似然估计方法，适用于荷兰早产儿随访数据分析。
The modelling of correlated binary outcomes, in such a way that the marginal response probabilities are still logistic, is considered. Different association measures for the dependence between correlated observations are discussed. For paired correlated data the full likelihood can be evaluated; for an arbitrary number of correlated observations a pseudolikelihood approach to obtain parameter estimates is proposed. The results are illustrated on data from a Dutch follow-up study on preterm infants