Melton B, Liang K Y, Pulver A E
Department of Biostatistics, School of Hygiene and Public Health, Johns Hopkins University, Baltimore, Maryland 21205.
Genet Epidemiol. 1994;11(4):311-27. doi: 10.1002/gepi.1370110402.
When no method exists for detecting genetic forms of a disorder, epidemiologists classify probands according to the presence or absence of an affected relative (familial or sporadic). Not only is this a surrogate measure but if the risk for the disorder is associated with characteristics such as age and gender, then probands with varied distributions of these characteristics among their relatives are subject to misclassification. A latent class approach is presented which explicitly models the relationship between the affected status of the relatives and the unobservable familial/sporadic status of the proband in order to adjust for these characteristics. Lastly, an approach is introduced to correct for attenuation in measures of association between familial/sporadic status and other variables that could result if probands are misclassified. This approach incorporates the latent class probabilities directly into the regression model without classifying probands. These methods are applied to a study of the heterogeneity of schizophrenia.
当不存在检测疾病遗传形式的方法时，流行病学家会根据是否存在患病亲属（家族性或散发性）对先证者进行分类。这不仅是一种替代测量方法，而且如果疾病风险与年龄和性别等特征相关，那么其亲属中这些特征分布各异的先证者就会被错误分类。本文提出了一种潜在类别方法，该方法明确地对亲属的患病状态与先证者不可观察的家族性/散发性状态之间的关系进行建模，以便对这些特征进行调整。最后，引入了一种方法来校正家族性/散发性状态与其他变量之间关联测量中的衰减，这种衰减可能是由于先证者被错误分类而导致的。该方法将潜在类别概率直接纳入回归模型，而不对先证者进行分类。这些方法应用于一项关于精神分裂症异质性的研究。
