Hopper J L, Derrick P L
Genet Epidemiol Suppl. 1986;1:73-82. doi: 10.1002/gepi.1370030712.
A pedigree model for binary data, motivated by log-linear modelling, has been developed to examine evidence for familial aggregation in disease status. From an epidemiological point of view a convenient way to express disease concordance between a pair of relatives is in terms of the odds ratio. For a rare disease this is almost equivalent to the relative risk of one family member being affected given that the other is affected, and in extending this to pedigrees it is assumed that these relative risks are multiplicative. In applying the model to the breast cancer data, pedigrees on a rare disease ascertained through an affected proband, it has been shown that estimation of concordance is dependent critically on knowing the probability that a sampled individual is affected. Therefore known population estimates of prevalence or cumulative risk, and an appropriate ascertainment correction, need to be invoked for the model to give proper estimates of disease concordance. The model is flexible in that measured ancillary risk factors, including genetic marker information, can be incorporated into the analysis. Therefore in future studies this information should be collected on all individuals, not just those affected. Suggested statistics for examining a fitted model are presented.
基于对数线性模型开发了一种用于二元数据的家系模型，以检验疾病状态下家族聚集的证据。从流行病学的角度来看，表达一对亲属之间疾病一致性的一种便捷方法是用优势比。对于罕见病，这几乎等同于一个家庭成员在另一个家庭成员患病的情况下患病的相对风险，并且在将其扩展到家系时，假设这些相对风险是可乘的。在将该模型应用于通过患病先证者确定的罕见病乳腺癌数据时，已经表明一致性的估计关键取决于了解抽样个体患病的概率。因此，为了使模型能够给出疾病一致性的正确估计，需要调用已知的总体患病率或累积风险估计值以及适当的确诊校正。该模型具有灵活性，因为可以将测量的辅助风险因素，包括基因标记信息，纳入分析。因此，在未来的研究中，应该收集所有个体的这些信息，而不仅仅是患病个体的信息。还给出了用于检验拟合模型的建议统计量。
