We examine four important considerations for the development of covariate adjustment methodologies in the context of 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：我们探讨了在间接治疗比较背景下开发协变量调整方法时的四个重要考量。首先，我们考量了加权法相较于结果建模法的潜在优势，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情境下，为何可能需要且有必要进行基于模型的外推。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，我们就双重稳健协变量调整框架的前景提供了进一步见解。