Methodological considerations for novel approaches to covariate-adjusted indirect treatment comparisons

Fuente: arXiv
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Main Authors: Remiro-Azócar, Antonio, Heath, Anna, Baio, Gianluca
Format: Preprint
Published: 2023
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author Remiro-Azócar, Antonio
Heath, Anna
Baio, Gianluca
author_facet Remiro-Azócar, Antonio
Heath, Anna
Baio, Gianluca
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2305_08651
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Methodological considerations for novel approaches to covariate-adjusted indirect treatment comparisons
Remiro-Azócar, Antonio
Heath, Anna
Baio, Gianluca
Methodology
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.
title Methodological considerations for novel approaches to covariate-adjusted indirect treatment comparisons
topic Methodology
url https://arxiv.org/abs/2305.08651