Inference for odds ratio regression models with sparse dependent data.
Inference for odds ratio regression models with sparse dependent data.
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DOI：
10.2307/2534002
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发表时间：
1998-03
期刊：
影响因子：
1.9
通讯作者：
J. Hanfelt;K. Liang
中科院分区：
文献类型：
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作者：
J. Hanfelt;K. Liang
假设2x2表的数量相对于平均表大小很大，并且给定表内的观察结果是依赖的，就像在纵向或基于家庭的病例对照研究中所发生的那样。我们考虑使用表格级别的协变量将回归模型与优势比进行拟合。重点是在相关结构未知的情况下，获得回归参数β的有效推断的方法。在这一背景下，梁(1985，Bitoiska 72,678-682)已经证明了基于非中心超几何可能性的推理对依赖结构的错误描述是敏感的。相比之下，基于Mantel-Haenszel方法的估计函数产生了一致的贝塔估计。在本文中，我们证明了在估计函数方法下，Wald的贝塔可信区间在乘性回归模型中表现良好，但不幸的是，当采用加性回归模型时，其覆盖概率较差。作为Wald推断的替代，我们提出了一种基于积分Mantel-Haenszel估计函数的Mantel-Haenszel拟似然函数。模拟研究表明，在中等样本下，Mantel-Haenszel拟似然方法在加性回归模型下比其他方法具有更好的推论效果，而在乘法模型下与Wald方法的推论相当。我们举例说明了这种准似然方法在精神分裂症家族风险研究中的应用。
Suppose the number of 2 x 2 tables is large relative to the average table size, and the observations within a given table are dependent, as occurs in longitudinal or family-based case-control studies. We consider fitting regression models to the odds ratios using table-level covariates. The focus is on methods to obtain valid inferences for the regression parameters beta when the dependence structure is unknown. In this setting, Liang (1985, Biometrika 72, 678-682) has shown that inference based on the noncentral hypergeometric likelihood is sensitive to misspecification of the dependence structure. In contrast, estimating functions based on the Mantel-Haenszel method yield consistent estimators of beta. We show here that, under the estimating function approach, Wald's confidence interval for beta performs well in multiplicative regression models but unfortunately has poor coverage probabilities when an additive regression model is adopted. As an alternative to Wald inference, we present a Mantel-Haenszel quasi-likelihood function based on integrating the Mantel-Haenszel estimating function. A simulation study demonstrates that, in medium-sized samples, the Mantel-Haenszel quasi-likelihood approach yields better inferences than other methods under an additive regression model and inferences comparable to Wald's method under a multiplicative model. We illustrate the use of this quasi-likelihood method in a study of the familial risk of schizophrenia.