Background: Multistate models (MSMs) applied to screening data can characterise the natural history of cancer and predict "stage-shifts" from screening. However, inferring parameters like mean sojourn time (MST) is challenging as disease onset is inherently unobserved in these data. This is even more challenging when characterising heterogeneity between cancer types in multicancer early detection (MCED) trial data. Methods: We utilised simulated longitudinal MCED screening datasets to evaluate the inferential bounds of MSMs under increasing clinical disaggregation: a 3-state (overall MST), 5-state (early/late stage), and 9-state (stages I-IV) model. Bayesian estimation was performed via Markov chain Monte Carlo. Robustness was assessed through chain convergence, parameter identifiability (via profile likelihood), and precision of estimates. We also explored hierarchical models and the use of informative priors to improve identifiability. Results: Based only on MCED trial data, many cancer types exhibited inferential challenges. Generally, the 5-state model was as robust as the 3-state model, showing slight improvements to convergence and identifiability while maintaining precision for overall MST. In contrast, the 9-state model showed worsened convergence and identifiability, and a significant reduction in the precision of overall MST estimates. Hierarchical models successfully improved performance, as have informative prior models but the latter introduced bias towards the prior values. Conclusions: While disaggregating natural history models by individual cancer stages is desirable for policy, these higher-dimensional models show a greater reliance on external data/assumptions. We recommend explicit identifiability assessments and assessments of the influence of external data/assumptions to support inference for MCED screening evaluations.

翻译：背景：将多状态模型应用于筛查数据可刻画癌症的自然史并预测筛查导致的“分期转移”。然而，由于疾病发病在这些数据中 inherently 不可观测，推断平均逗留时间等参数极具挑战性。当在多癌早筛试验数据中刻画不同癌症类型间的异质性时，这一挑战更为严峻。方法：我们利用模拟的纵向多癌早筛数据集，评估在递增的临床细分程度下（三状态模型（总体平均逗留时间）、五状态模型（早/晚期）及九状态模型（I-IV 期））多状态模型的推断边界。通过马尔可夫链蒙特卡洛方法进行贝叶斯估计，并基于链收敛性、参数可识别性（通过轮廓似然法）及估计精度评估其稳健性。我们还探索了分层模型及使用信息先验以提升可识别性。结果：仅基于多癌早筛试验数据，多种癌症类型即面临推断困难。总体而言，五状态模型与三状态模型具有相当的稳健性，在保持总体平均逗留时间精度的同时，对收敛性和可识别性略有改善。相比之下，九状态模型的收敛性和可识别性恶化，且总体平均逗留时间估计精度显著下降。分层模型成功提升了模型性能，信息先验模型同样有效，但后者会引入偏向先验值的偏差。结论：尽管按个体癌症分期分解自然史模型对政策制定有利，但此类高维模型对外部数据/假设的依赖性更强。我们建议开展明确的可识别性评估及外部数据/假设影响评估，以支撑多癌早筛查评估的推断。