Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway.
Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
Stat Med. 2023 Oct 15;42(23):4207-4235. doi: 10.1002/sim.9856. Epub 2023 Aug 1.
Additive frailty models are used to model correlated survival data. However, the complexity of the models increases with cluster size to the extent that practical usage becomes increasingly challenging. We present a modification of the additive genetic gamma frailty (AGGF) model, the lean AGGF (L-AGGF) model, which alleviates some of these challenges by using a leaner additive decomposition of the frailty. The performances of the models were compared and evaluated in a simulation study. The L-AGGF model was used to analyze population-wide data on clustering of melanoma in 2 391 125 two-generational Norwegian families, 1960-2015. Using this model, we could analyze the complete data set, while the original model limited the analysis to a restricted data set (with cluster sizes ). We found a substantial clustering of melanoma in Norwegian families and large heterogeneity in melanoma risk across the population, where 52% of the frailty was attributed to the 10% of the population at highest unobserved risk. Due to the improved scalability, the L-AGGF model enables a wider range of analyses of population-wide data compared to the AGGF model. Moreover, the methods outlined here make it possible to perform these analyses in a computationally efficient manner.
加性脆弱模型用于对相关生存数据进行建模。然而，模型的复杂性随着簇大小的增加而增加，以至于实际使用变得越来越具有挑战性。我们提出了对加性遗传伽马脆弱性（AGGF）模型的一种修改，即精简的 AGGF（L-AGGF）模型，通过对脆弱性进行更精简的加性分解，缓解了这些挑战中的一些。在模拟研究中比较和评估了模型的性能。该模型用于分析了 1960 年至 2015 年间在 2391125 个挪威两辈家庭中黑色素瘤聚类的全人群数据。使用这个模型，我们可以分析完整的数据集，而原始模型将分析限制在一个受限的数据集（簇大小为 ）。我们发现挪威家庭中的黑色素瘤存在大量聚类，人群中黑色素瘤风险存在很大的异质性，其中 52%的脆弱性归因于处于最高未观察到风险的 10%人群。由于可扩展性的提高，与 AGGF 模型相比，L-AGGF 模型能够更广泛地分析全人群数据。此外，这里概述的方法使在计算上有效地进行这些分析成为可能。
