We examine four important considerations in the development of covariate adjustment methodologies for indirect treatment comparisons. Firstly, we consider potential advantages of weighting versus outcome modeling, placing focus on bias-robustness. Secondly, we outline why model-based extrapolation may be required and useful, in the specific context of indirect treatment comparisons with limited overlap. Thirdly, we describe challenges for covariate adjustment based on data-adaptive outcome modeling. Finally, we offer further perspectives on the promise of doubly-robust covariate adjustment frameworks.

翻译：我们探讨了在开发用于间接治疗比较的协变量调整方法时的四个重要考量因素。首先，我们考虑了加权法与结果建模相比的潜在优势，重点关注偏倚稳健性。其次，我们概述了在重叠有限的间接治疗比较特定背景下，为何基于模型的推断可能是必要且有用的。第三，我们描述了基于数据自适应结果建模进行协变量调整所面临的挑战。最后，我们进一步阐述了双重稳健协变量调整框架的前景。