Work

Application of Multi-level Network Meta-Regression in the NICE Technology Appraisal of Quizartinib for Induction, Consolidation and Maintenance Treatment of Newly Diagnosed FLT3-ITD-Positive Acute Myeloid Leukaemia: An External Assessment Group Perspective

Year: 2024

Type: article

Abstract: Matching-adjusted indirect comparisons (MAICs) [ 1 ] are increasingly popular within National Institute for Health and Care Excellence (NICE) Single Technology Appraisals (STAs) as a method to adjust for cross-study differences in patient characteristics, which are treatment-effect modifiers [ 2 , 3 ]. Matching-adjusted indirect comparisons are applicable only in a two-study indirect treatment comparison (ITC) scenario where individual participant data (IPD) are available from a study comparing treatment A versus treatment C and aggregate data (AD) from a second study comparing treatment B versus treatment C, to obtain the indirect comparison of A versus B. An inherent limitation is that MAICs provide comparative effect estimates that are applicable only to the population of the AD study and cannot be transposed to different populations [ 4 ]. Multi-level network meta-regression (ML-NMR) overcomes these limitations, allowing flexibility to generate population-adjusted ITC estimates from any number of treatments and studies, which are applicable to any specified target population [ 4 , 5 , 6 ].

Source: PharmacoEconomics

DOI: 10.1007/s40273-024-01460-1

Authors: Sarah J. Nevitt, David M. Phillippo, Robert Hodgson, Nicky J. Welton, Sofia Dias

Institutions: University of York, University of Bristol

Language: English

Topic: Health Systems, Economic Evaluations, Quality of Life

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,790