Gene therapies aim to address the root causes of diseases, particularly those stemming from rare genetic defects that can be life-threatening or severely debilitating. Although an increasing number of gene therapies have received regulatory approvals in recent years, understanding their long-term efficacy in trials with limited follow-up time remains challenging. To address this critical question, we propose a novel Bayesian framework designed to selectively integrate relevant external data with internal trial data to improve the inference of the durability of long-term efficacy. We proved that the proposed method has desired theoretical properties, such as identifying and favoring external subsets deemed relevant, where the relevance is defined as the similarity, induced by the marginal likelihood, between the generating mechanisms of the internal data and the selected external data. We also conducted comprehensive simulations to evaluate its performance under various scenarios. Furthermore, we apply this method to predict and infer the endogenous factor IX (FIX) levels of patients who receive Etranacogene dezaparvovec over the long-term. Our estimated long-term FIX levels, validated by recent trial data, indicate that Etranacogene dezaparvovec induces sustained FIX production. Together, the theoretical findings, simulation results, and successful application of this framework underscore its potential to address similar long-term effectiveness estimation and inference questions in real world applications.

翻译：基因治疗旨在解决疾病的根本原因，特别是那些源于罕见遗传缺陷、可能危及生命或严重致残的疾病。尽管近年来获得监管批准的基因疗法数量不断增加，但在随访时间有限的试验中理解其长期疗效仍然具有挑战性。为解决这一关键问题，我们提出了一种新颖的贝叶斯框架，旨在选择性地整合相关外部数据与内部试验数据，以改进长期疗效持久性的推断。我们证明了所提方法具有理想的理论特性，例如能够识别并倾向于被视为相关的外部数据子集，其中相关性定义为内部数据与所选外部数据的生成机制之间，由边际似然诱导的相似性。我们还进行了全面的模拟研究，以评估其在各种场景下的性能。此外，我们将此方法应用于预测和推断接受Etranacogene dezaparvovec治疗的患者长期的内源性凝血因子IX（FIX）水平。我们估计的长期FIX水平（已通过近期试验数据验证）表明，Etranacogene dezaparvovec能够诱导持续的FIX生成。综合来看，理论发现、模拟结果以及该框架的成功应用，共同突显了其在解决现实应用中类似长期有效性估计与推断问题方面的潜力。