Open AccessApplication of quantitative bias analysis for unmeasured confounding in cost–effectiveness modelling
Author(s) -
Thomas Leahy,
Stephen Duffield,
Seamus Kent,
Cormac Sammon,
Dimitrios Tzelis,
Joshua Ray,
Rolf H.H. Groenwold,
Manuel Gomes,
Sreeram Ramagopalan,
Richard Grieve
Publication year - 2022
Publication title -
journal of comparative effectiveness research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.567
H-Index - 23
eISSN - 2042-6313
pISSN - 2042-6305
DOI - 10.2217/cer-2022-0030
Subject(s) - confounding , context (archaeology) , medicine , econometrics , economics , paleontology , pathology , biology
Due to uncertainty regarding the potential impact of unmeasured confounding, health technology assessment (HTA) agencies often disregard evidence from nonrandomized studies when considering new technologies. Quantitative bias analysis (QBA) methods provide a means to quantify this uncertainty but have not been widely used in the HTA setting, particularly in the context of cost-effectiveness modelling (CEM). This study demonstrated the application of an aggregate and patient-level QBA approach to quantify and adjust for unmeasured confounding in a simulated nonrandomized comparison of survival outcomes. Application of the QBA output within a CEM through deterministic and probabilistic sensitivity analyses and under different scenarios of knowledge of an unmeasured confounder demonstrates the potential value of QBA in HTA.