AI 中文总结
本文介绍多状态模型的多级网络荟萃回归（ML-NMR-MS），将ML-NMR整合的IPD加AgD似然嵌入疾病-死亡多状态模型，解决协变量不平衡问题，产生人群调整的联合PFS/OS治疗效果，可直接参数化状态转换经济模型。
AI 中文摘要
在肿瘤学事件发生时间结局的网络荟萃分析（NMA）中，越来越多的人认识到无进展生存期（PFS）和总生存期（OS）联合分析比单独分析更好。Jansen等人的多状态NMA通过拟合汇总生存曲线的三状态（稳定、进展、死亡）马尔可夫模型来解决此问题，但它无法调整试验间效应修饰协变量的不平衡，因此其相对效应可能存在偏差或与目标人群无关。多级网络荟萃回归（ML-NMR）通过在汇总协变量分布上整合个体水平模型来解决协变量不平衡问题，将个体参与者数据（IPD）和汇总数据（AgD）连贯地结合起来；然而，它仅针对单终点结局开发。本文介绍了多状态模型的多级网络荟萃回归（ML-NMR-MS），它将ML-NMR整合的IPD加AgD似然嵌入疾病-死亡多状态模型中。对于IPD研究，使用链接转换上的完整多状态似然；对于AgD研究，通过在重建的协变量分布上对转换强度进行准蒙特卡罗积分来计算协变量边际状态占用概率，并通过条件生存似然输入。这产生了人群调整的联合PFS/OS治疗效果，并可以直接参数化状态转换经济模型。在一个说明性的肿瘤学网络上，ML-NMR-MS将治疗的联合效应分解为其组成转换，将对进展的效应与进展后生存的效应分开，并产生条件和边际效应。一项真相完全已知的模拟研究证实了在正确设定下的无偏恢复和接近名义覆盖。
英文摘要
In network meta-analysis (NMA) of oncology time-to-event outcomes, there is growing recognition that progression-free survival (PFS) and overall survival (OS) are better synthesized jointly than in separate analyses. The multistate NMA of Jansen et al. addresses this with a tri-state (stable, progressed, dead) Markov model fitted to aggregate survival curves, but it cannot adjust for imbalance in effect-modifying covariates across trials, so its relative effects may be biased or not relevant for the target population of interest. Multilevel network meta-regression (ML-NMR) resolves covariate imbalance by integrating an individual-level model over the aggregate covariate distribution, coherently combining individual participant data (IPD) and aggregate data (AgD); however, it has been developed only for single-endpoint outcomes. This paper introduces multilevel network meta-regression for multistate models (ML-NMR-MS), which embeds the ML-NMR integrated IPD-plus-AgD likelihood inside an illness--death multistate model. For IPD studies the full multistate likelihood over linked transitions is used; for AgD studies covariate-marginal state-occupancy probabilities are computed by quasi-Monte-Carlo integration of the occupancies implied by the transition intensities over the reconstructed covariate distribution, and entered through a conditional-survival likelihood. This yields population-adjusted joint PFS/OS treatment effects and can directly parameterize a state-transition economic model. On an illustrative oncology network, ML-NMR-MS resolves a treatment's joint effect into its component transitions, separating an effect on progression from an effect on post-progression survival, and produces conditional and marginal effects. A simulation study with fully known truth confirms unbiased recovery and near-nominal coverage under correct specification.
