The paper outlines four considerations for improving covariate adjustment in indirect treatment comparisons, focusing on bias-robustness, extrapolation needs, data-adaptive challenges, and doubly-robust methods.
Bayesian regression tree models fo r causal inference: Regularization, confounding, and heterogeneous effects (with discussion)
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Methodological considerations for novel approaches to covariate-adjusted indirect treatment comparisons
The paper outlines four considerations for improving covariate adjustment in indirect treatment comparisons, focusing on bias-robustness, extrapolation needs, data-adaptive challenges, and doubly-robust methods.