双胞胎数据删失的可靠性门限模型
The Liability Threshold Model for Censored Twin Data
摘要：
家庭研究是了解疾病病因的重要工具，其主要目的是发现家庭聚集的证据，并确定这种聚集是否可归因于遗传成分。在双生子疾病研究中，通常会计算遗传力和一致性估计值，作为量化这种遗传贡献的一种方式。这些研究中的终点通常定义为疾病发生与非疾病死亡。然而，大部分受试者在随访时可能仍然活着，没有经历过疾病，因此仍然处于风险中。忽略这种右删失可能导致严重的估计偏差。我们建议扩展经典的责任阈值模型与完全观测值截尾加权的逆概率。这导致了一种灵活的方式来模拟双胞胎一致性，并获得遗传力的相合估计。我们应用该方法在模拟和数据的人口为基础的丹麦双胞胎队列，我们描述了在双胞胎中的前列腺癌发生的依赖。
英文摘要：
Family studies provide an important tool for understanding etiology of diseases, with the key aim of discovering evidence of family aggregation and to determine if such aggregation can be attributed to genetic components. Heritability and concordance estimates are routinely calculated in twin studies of diseases, as a way of quantifying such genetic contribution. The endpoint in these studies are typically defined as occurrence of a disease versus death without the disease. However, a large fraction of the subjects may still be alive at the time of follow-up without having experienced the disease thus still being at risk. Ignoring this right-censoring can lead to severely biased estimates. We propose to extend the classical liability threshold model with inverse probability of censoring weighting of complete observations. This leads to a flexible way of modeling twin concordance and obtaining consistent estimates of heritability. We apply the method in simulations and to data from the population based Danish twin cohort where we describe the dependence in prostate cancer occurrence in twins.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
