  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.
