Are you passionate about using some if not all of your prior knowledge to inform your decisions? Then this educational tutorial on how to perform Bayesian meta-analysis of health state utility values using R is a must-read for you! https://lnkd.in/deaFXJ7Z Bayesian meta-analysis stands out for several reasons: 1. Valuable prior information: Integrates previous study findings to enhance the meta-analysis, providing more robust and precise estimates. 2. Explicit heterogeneity handling: More transparently handles the degree of heterogeneity among studies, which is crucial since utility values can be highly variable across studies. 3. Enhanced uncertainty assessment: Generates comprehensive posterior distributions, allowing for better assessment of uncertainties around estimates. This can be incorporated into probabilistic sensitivity analysis during cost-utility analyses—enabling clearer interpretation and informed decision-making. Imagine harnessing these capabilities to produce precise, reliable estimates and make well-informed inferences about utility values. Be empowered—embrace Bayesian meta-analysis in your health economic modelling projects. After all, Bayesian is just intuitive! Thank you, Joseph Alvin Ramos Santos, Robert Grant, and Gian Luca Di Tanna, for sharing your knowledge and empowering us!! #Research #BayesianAnalysis #MetaAnalysis #HSUV
Full Professor of BioStatistics and Health Economics, Head of Research, Department of Business Economics, Health & Social Care (DEASS), University of Applied Sciences and Arts of Southern Switzerland
We are pleased to share our latest paper, "Bayesian Meta-Analysis of Health State Utility Values: A Tutorial with Practical Application in Heart Failure," published in #PharmacoEconomics. https://lnkd.in/deaFXJ7Z This tutorial is designed to support researchers in generating robust pooled estimates of health state utility values to inform economic evaluations. Specifically, this work shows the applicability of #Bayesian #meta-analysis for pooling health state utility values through an illustrative example among patients with heart failure. Using data from a systematic review encompassing 21 studies and ready-to-use codes and scripts, this study provides a step-by-step guide for conducting Bayesian meta-analysis using #R. The tutorial adheres to the structured workflow as follows: - Setting up the data - Imputing missing standard deviations - Defining the priors - Fitting the model - Diagnosing model convergence - Interpreting results - Performing sensitivity analyses We showed that the pooled utility value for #HeartFailure was 0.66 with a 95% credible interval of [0.60, 0.70] which was consistent across a series of scenarios according to the imputation of missing standard deviations. This tutorial aimed to foster interest in Bayesian methods and their application to bridge the gap in estimating health state utility values for economic evaluations and policy decisions. We encourage the adoption of Bayesian meta-analysis in synthesising utility values, as it offers a principled means to integrate prior knowledge and handle heterogeneity, leading to intuitive probabilistic interpretations that are critical in decision making. Robert Grant Chris Carswell
Full Professor of BioStatistics and Health Economics, Head of Research, Department of Business Economics, Health & Social Care (DEASS), University of Applied Sciences and Arts of Southern Switzerland
7moThank you Trust for sharing our work, much appreciated!