AIM：多状态转换模型的摊销推理
AIM: Amortized Inference for Multistate Transition Models
AI总结：
研究针对区间删失连续时间多状态转换模型，提出摊销贝叶斯推理框架AIM。通过离线训练学习汇总统计量与模型参数关系，无需重复似然性优化。证明相关特性，模拟研究显示其能准确估计和量化不确定性，推理速度大幅提升，是可扩展且有理论基础的推理框架。
AI中文摘要：
区间删失连续时间多状态转换模型（MSTMs）被广泛用于描述疾病进展和其他动态过程。现有推理程序主要基于似然性，且每个数据集都需单独拟合模型，涉及对转换概率矩阵的重复评估。我们提出AIM，一种用于区间删失连续时间MSTMs的摊销贝叶斯推理框架。AIM在离线训练阶段从模拟数据集中学习信息性汇总统计量与模型参数间的关系，训练后无需重复似然性优化即可为新数据集提供后验推理。在固定观察计划和离散协变量分层下，证明了区间特定转换计数对观察面板似然性的充分性及相应总体汇总的可识别性，还证明了诱导汇总后验及其神经近似的一致性。模拟研究表明AIM能进行准确点估计和可靠不确定性量化，在线推理速度比基于重复似然性估计快154至2308倍。这些结果确立了AIM作为区间删失MSTMs中可扩展且理论基础扎实的可重复使用贝叶斯推理框架的地位。
英文摘要：
Interval censored continuous time multistate transition models (MSTMs) are widely used to characterize disease progression and other dynamic processes. Existing inference procedures are predominantly likelihood-based and require a separate model fit for each dataset, involving repeated evaluation of transition probability matrices. This repeated computation can become burdensome when the same model is applied across centers, time periods, or newly collected cohorts. We propose AIM, an amortized Bayesian inference framework for interval censored continuous time MSTMs. AIM learns the relationship between informative summary statistics and model parameters from simulated datasets generated during an offline training stage. Once trained for a prespecified model class, AIM provides posterior inference for new datasets without repeated likelihood optimization. Under fixed observation schedules and discretized covariate strata, we show that interval-specific transition counts are sufficient for the observed panel likelihood and establish identifiability of the corresponding population summaries. We further prove consistency of the induced summary posterior and its neural approximation, yielding a consistent posterior mean estimator. Simulation studies under progressive and competing-pathway MSTMs demonstrate accurate point estimation and reliable uncertainty quantification. AIM completed online inference in milliseconds, with median speedups ranging from approximately 154-fold to 2308-fold relative to repeated likelihood-based estimation. These results establish AIM as a scalable and theoretically grounded framework for reusable Bayesian inference in interval censored MSTMs.
