Open AccessUnmeasured confounding in nonrandomized studies: quantitative bias analysis in health technology assessment
Author(s) -
Thomas Leahy,
Seamus Kent,
Cormac Sammon,
Rolf H.H. Groenwold,
Richard Grieve,
Sreeram V Ramagopalan,
Manuel Gomes
Publication year - 2022
Publication title -
journal of comparative effectiveness research
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 0.567
H-Index - 23
eISSN - 2042-6313
pISSN - 2042-6305
DOI - 10.2217/cer-2022-0029
Subject(s) - confounding , medicine , health technology , intensive care medicine , medline , criticism , actuarial science , risk analysis (engineering) , health care , economics , economic growth , art , literature , political science , law
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, which has led to much criticism of NRS by HTA agencies. Quantitative bias analyses are a group of methods that have been developed in the epidemiological literature to quantify the impact of unmeasured confounding and adjust effect estimates from NRS. Key considerations for application in HTA proposed in this article reflect the need to balance methodological complexity with ease of application and interpretation, and the need to ensure the methods fit within the existing frameworks used to assess nonrandomized evidence by HTA bodies.