Meta-analytic estimation of measurement variability and assessment of its impact on decision-making: the case of perioperative haemoglobin concentration monitoring

BMC Medical Research Methodology
Emmanuel CharpentierLoïc Guillevin

Abstract

As a part of a larger Health Technology Assessment (HTA), the measurement error of a device used to monitor the hemoglobin concentration of a patient undergoing surgery, as well as its decision consequences, were to be estimated from published data. A Bayesian hierarchical model of measurement error, allowing the meta-analytic estimation of both central and dispersion parameters (under the assumption of normality of measurement errors) is proposed and applied to published data; the resulting potential decision errors are deduced from this estimation. The same method is used to assess the impact of an initial calibration. The posterior distributions are summarized as mean ± sd (credible interval). The fitted model exhibits a modest mean expected error (0.24 ± 0.73 (-1.23 1.59) g/dL) and a large variability (mean absolute expected error 1.18 ± 0.92 (0.05 3.36) g/dL). The initial calibration modifies the bias (-0.20 ± 0.87 (-1.99 1.49) g/dL), but the variability remains almost as large (mean absolute expected error 1.05 ± 0.87 (0.04 3.21) g/dL). This entails a potential decision error ("false positive" or "false negative") for about one patient out of seven. The proposed hierarchical model allows the estimation of the variability ...Continue Reading

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Citations

Nov 29, 2017·Pharmaceutical Statistics·Tobias MützeTim Friede
May 28, 2020·BMC Health Services Research·Sutapa Bandyopadhyay NeogiRenu Saxena
Dec 15, 2018·Statistics in Medicine·Gaelle Saint-HilaryGianluca Mastrantonio

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