Multi-stage disease histories derived from longitudinal data are becoming increasingly available as registry data and biobanks expand. Multi-state models are suitable to investigate transitions between different disease stages in presence of competing risks. In this context, however, their estimation is complicated by dependent left-truncation, multiple time scales, index event bias, and interval-censoring. In this work, we investigate the extension of piecewise exponential additive models (PAMs) to this setting and their applicability given the above challenges. In simulation studies we show that PAMs can handle dependent left-truncation and accommodate multiple time scales. Compared to a stratified single time scale model, a multiple time scales model is found to be less robust to the data generating process. We also quantify the extent of index event bias in multiple settings, demonstrating its dependence on the completeness of covariate adjustment. In general, PAMs recover baseline and fixed effects well in most settings, except for baseline hazards in interval-censored data. Finally, we apply our framework to estimate multi-state transition hazards and probabilities of chronic kidney disease (CKD) onset and progression in a UK Biobank dataset (n=142,667). We observe CKD progression risk to be highest for individuals with early CKD onset and to further increase over age. In addition, the well-known genetic variant rs77924615 in the UMOD locus is found to be associated with CKD onset hazards, but not with risk of further CKD progression.

翻译：随着登记数据和生物样本库的扩展，从纵向数据衍生的多阶段疾病史正变得越来越可获得。多状态模型适用于研究存在竞争风险时不同疾病阶段间的转移。然而在此背景下，其估计因相依左截断、多时间尺度、索引事件偏倚及区间删失而变得复杂。在本工作中，我们研究了分段指数加性模型在此场景下的扩展及其面对上述挑战的适用性。在模拟研究中，我们表明分段指数加性模型能够处理相依左截断并适应多时间尺度。与分层的单时间尺度模型相比，多时间尺度模型对数据生成过程的稳健性较低。我们还量化了多种设置下索引事件偏倚的程度，证明了其对协变量调整完整性的依赖。总体而言，除区间删失数据中的基线风险外，分段指数加性模型在大多数设置下能较好地恢复基线效应和固定效应。最后，我们将该框架应用于英国生物样本库数据集（n=142,667），以估计慢性肾脏病发病与进展的多状态转移风险和概率。我们观察到，对于早期发病的个体，慢性肾脏病进展风险最高，并随年龄进一步增加。此外，已知的UMOD基因座遗传变异rs77924615被发现与慢性肾脏病发病风险相关，但与进一步进展的风险无关。