Frequentist and Bayesian meta‐regression of health state utilities for multiple myeloma incorporating systematic review and analysis of individual patient data

Anthony J. Hatswell, Darren Burns, Gianluca Baio and Frances Wadelin

Health Economics, 2019, vol. 28, issue 5, 653-665

Abstract: This analysis presents the results of a systematic review for health state utilities in multiple myeloma, as well as analysis of over 9,000 observations taken from registry and trial data. The 27 values identified from 13 papers are then synthesised in a frequentist nonparametric bootstrap model and a Bayesian meta‐regression. Results were similar between the frequentist and Bayesian models with low utility on disease diagnosis (approximately 0.55), raising to approximately 0.65 on first line treatment and declining slightly with each subsequent line. Stem cell transplant was also found to be a significant predictor of health‐related quality of life in both individual patient data and meta‐regression, with an increased utility of approximately 0.06 across different models. The work presented demonstrates the feasibility of Bayesian methods for utility meta‐regression, whilst also presenting an internally consistent set of data from the analysis of registry data. To facilitate easy updating of the data and model, data extraction tables and model code are provided as Data S1. The main limitations of the model relate to the low number of studies available, particularly in highly pretreated patients.

Date: 2019
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https://doi.org/10.1002/hec.3871

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Persistent link: https://EconPapers.repec.org/RePEc:wly:hlthec:v:28:y:2019:i:5:p:653-665

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