Multilevel network meta-regression for multistate models: Population-adjusted joint synthesis of progression and survival data from individual and aggregate evidence
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多状态模型的多层网络元回归：联合综合个体与汇总证据并进行人群调整
- 研究问题
- 跨肿瘤试验的效应修饰变量不平衡时，如何联合综合无进展生存与总生存，并得到适用于目标人群的治疗效应？
- 方法与思路
- 作者将个体及汇总数据的多层元回归似然嵌入疾病—死亡模型，以准蒙特卡洛积分计算协变量边际化的状态占据概率。
- 材料陈述的贡献
- 摘要称该方法可分解进展与进展后生存的治疗效应；正确设定下的模拟显示无偏恢复及接近名义水平的覆盖率。
- 阅读关注点
- 应核对跨试验效应可比性、协变量分布重建、马尔可夫设定，以及汇总生存曲线如何进入联合似然。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未经核实；摘要未说明完整识别条件，正确设定下的模拟不能保证实际应用无偏。
原文摘要
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.