A growing number of oncology treatments, such as bevacizumab, are used across multiple indications. However, in health technology assessment (HTA), their clinical and cost-effectiveness are typically appraised within a single target indication. This approach excludes a broader evidence base across other indications. To address this, we explored multi-indication meta-analysis methods that share evidence across indications. We conducted a simulation study to evaluate alternative multi-indication synthesis models. This included univariate (mixture and non-mixture) methods synthesizing overall survival (OS) data and bivariate surrogacy models jointly modelling treatment effects on progression-free survival (PFS) and OS, pooling surrogacy parameters across indications. Simulated datasets were generated using a multistate disease progression model under various scenarios, including different levels of heterogeneity within and between indications, outlier indications, and varying data on OS for the target indication. We evaluated the performance of the synthesis models applied to the simulated datasets, in terms of their ability to predict overall survival (OS) in a target indication. The results showed univariate multi-indication methods could reduce uncertainty without increasing bias, particularly when OS data were available in the target indication. Compared with univariate methods, mixture models did not significantly improve performance and are not recommended for HTA. In scenarios where OS data in the target indication is absent and there were also outlier indications, bivariate surrogacy models showed promise in correcting bias relative to univariate models, though further research under realistic conditions is needed. Multi-indication methods are more complex than traditional approaches but can potentially reduce uncertainty in HTA decisions.

翻译：越来越多的肿瘤治疗方法（如贝伐珠单抗）被用于多种适应症。然而在卫生技术评估中，其临床与成本效益通常仅在单一目标适应症内进行评估。这种方法排除了其他适应症的更广泛证据基础。为此，我们探索了可在不同适应症间共享证据的多适应症荟萃分析方法。我们通过模拟研究评估了多种多适应症综合模型，包括合成总生存期数据的单变量（混合与非混合）方法，以及联合建模无进展生存期与总生存期治疗效应的双变量替代终点模型（该模型可跨适应症合并替代性参数）。我们利用多状态疾病进展模型在不同情境下生成模拟数据集，包括适应症内与适应症间异质性水平差异、离群适应症的存在以及目标适应症总生存期数据量的变化。通过评估这些综合模型在模拟数据集上预测目标适应症总生存期的性能，我们发现单变量多适应症方法能在不增加偏倚的前提下降低不确定性，尤其在目标适应症存在总生存期数据时效果显著。与单变量方法相比，混合模型未能显著提升性能，故不推荐用于卫生技术评估。在目标适应症缺乏总生存期数据且存在离群适应症的情况下，双变量替代终点模型相较于单变量模型展现出纠正偏倚的潜力，但尚需在真实条件下开展进一步研究。多适应症方法虽较传统方法更为复杂，但有望降低卫生技术评估决策的不确定性。