Non-collapsible marginal effect measures depend on joint distributions of effect modifiers and prognostic variables, so unadjusted anchored indirect comparisons can be biased even without individual-level treatment effect heterogeneity.
NICE DSU technical support document 18: methods for population-adjusted indirect comparisons in submissions to NICE
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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.
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Transportability of model-based estimands in evidence synthesis
Non-collapsible marginal effect measures depend on joint distributions of effect modifiers and prognostic variables, so unadjusted anchored indirect comparisons can be biased even without individual-level treatment effect heterogeneity.
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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.