MIM generates synthetic datasets via multiple imputation to average predictions from a parametric outcome model over the target covariate distribution, providing marginal effect estimates with proper uncertainty quantification.
arXiv preprint arXiv:2301.09661 (2023)
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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.
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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Model-based standardization using multiple imputation
MIM generates synthetic datasets via multiple imputation to average predictions from a parametric outcome model over the target covariate distribution, providing marginal effect estimates with proper uncertainty quantification.
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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.