Quantitative bias analysis for unmeasured confounding in unanchored population-adjusted indirect comparisons
Sign Up to like & getrecommendations! Published in 2025 at "Research Synthesis Methods"
DOI: 10.1017/rsm.2025.13
Abstract: Unanchored population-adjusted indirect comparisons (PAICs) such as matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC) attracted a significant attention in the health technology assessment field in recent years. These methods allow for indirect comparisons… read more here.
Keywords: indirect comparisons; quantitative bias; unanchored population; analysis ... See more keywords
Quantitative Bias Analysis for Collaborative Science.
Sign Up to like & getrecommendations! Published in 2018 at "Epidemiology"
DOI: 10.1097/ede.0000000000000875
Abstract: We used the study by Forns and colleagues1 to outline how quantitative bias analysis (QBA) can be applied to collaborative science projects. Our objective was to quantify the conditions necessary to yield the observed cohort-specific… read more here.
Keywords: bias analysis; collaborative science; quantitative bias;
Unmeasured confounding in nonrandomized studies: quantitative bias analysis in health technology assessment.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of comparative effectiveness research"
DOI: 10.2217/cer-2022-0029
Abstract: Evidence generated from nonrandomized studies (NRS) is increasingly submitted to health technology assessment (HTA) agencies. Unmeasured confounding is a primary concern with this type of evidence, as it may result in biased treatment effect estimates,… read more here.
Keywords: unmeasured confounding; health technology; quantitative bias; technology assessment ... See more keywords