We review the evolution of health economic evaluation practice in the Netherlands before and after the introduction of the ZIN’s 2016 guidelines. Based on some key components within the health economics framework addressed by the new guidelines, we specifically focus on reviewing the statistical methods, missing data methods and software implemented by health economists.
Linear mixed effects models (LMMs) offer a simple alternative to handle missing data under Missing At Random (MAR) without requiring imputations, and have not been very well explored in the CEA context. In this manuscript, we aim to familiarize readers with LMMs and demonstrate their implementation in trial-based cost-effectiveness analysis.With Catrin Plumpton and Sube Banerjee and Baptiste Leurent
We extend the current methods for modelling trial-based partitioned survival cost-utility data, taking advantage of the flexibility of the Bayesian approach, and specify a joint probabilistic model for the health economic outcomes. This allows to account for multiple types of data complexities and the dependence relationships between different types of quality of life and cost components.
In trial-based economic evaluation, restricting the analysis to the complete cases is inefficient and can result in biased estimates, while imputation methods are often implemented under a missing at random (MAR) assumption. We propose the use of joint longitudinal models to extend standard approaches by taking into account the longitudinal structure to improve the estimation of the targeted quantities under MAR.With Rachael Hunter and Alexina Mason and Gianluca Baio
missingHE is an R package aimed at providing tools to handle missing outcome data under a full Bayesian framework in economic evaluations. The package relies on the package R2jags to implement Bayesian models via JAGS and Markov Chain Monte Carlo (MCMC) methods. Different types of missing data models are implemented in the package, including selection models, pattern mixture models and hurdle models
We extend and adapt the modelling framework typically used for the analysis of football data for the analysis and prediction of volleyball results in regular seasons. Three different sub-models (points scored, number of sets, winner) which are jointly modelled using a flexible Bayesian parametric approach, which allows to fully propagate the uncertainty for each unobserved quantity and to assess the predictive performance of the model.
The primary role of economic evaluation for health technology assessment (HTA) is not the estimation of the quantities of interest but to aid decision making. Thus, standard frequentist analyses, who rely on power calculations and p-values, are not well-suited for addressing these HTA requirements. Rather, HTA should embrace a decision-theoretic paradigm to inform two decisionsWith Andrea Manca and Gianluca Baio
The purpose of this review is to critically appraise the current literature in within-trial CEAs with respect to the quality of the information reported and the methods used to deal with missingness for both effectiveness and costs. The review complements previous work, covering 2003-2009 (88 articles) with a new systematic review, covering 2009-2015 (81 articles) and focuses on two perspectivesWith Alexina Mason and Gianluca Baio
Using a recent randomised trial as our motivating example, we present a Bayesian parametric model for conducting inference on a bivariate health economic longitudinal response. We specify our model to account for the different types of complexities affecting the data while accommodating a sensitivity analysis to explore the impact of alternative missingness assumptions on the inferences and on the decision-making process for health technology assessmentWith Michael Daniels and Gianluca Baio
Our proposed modelling framework allows jointly tackling of the different complexities that affect the data in a relatively easy way by means of its modular structure and flexible choice for the distributions of the QALYs and cost variables. We specifically focus on appropriately modelling spikes at the boundary and missingness, as they have substantial implications in terms of cost-effectiveness results.With Alexina Mason and Gianluca Baio
Linear mixed effects models (LMMs) offer a simple alternative to handle missing data under Missing At Random (MAR) without requiring imputations, and have not been very well explored in the CEA context. In this manuscript, we aim to familiarize readers with LMMs and demonstrate their implementation in trial-based cost-effectiveness analysis.With Catrin Plumpton and Sube Banerjee and Baptiste Leurent
In trial-based economic evaluation, restricting the analysis to the complete cases is inefficient and can result in biased estimates, while imputation methods are often implemented under a missing at random (MAR) assumption. We propose the use of joint longitudinal models to extend standard approaches by taking into account the longitudinal structure to improve the estimation of the targeted quantities under MAR.With Rachael Hunter and Alexina Mason and Gianluca Baio
missingHE is an R package aimed at providing tools to handle missing outcome data under a full Bayesian framework in economic evaluations. The package relies on the package R2jags to implement Bayesian models via JAGS and Markov Chain Monte Carlo (MCMC) methods. Different types of missing data models are implemented in the package, including selection models, pattern mixture models and hurdle models
The purpose of this review is to critically appraise the current literature in within-trial CEAs with respect to the quality of the information reported and the methods used to deal with missingness for both effectiveness and costs. The review complements previous work, covering 2003-2009 (88 articles) with a new systematic review, covering 2009-2015 (81 articles) and focuses on two perspectivesWith Alexina Mason and Gianluca Baio
Using a recent randomised trial as our motivating example, we present a Bayesian parametric model for conducting inference on a bivariate health economic longitudinal response. We specify our model to account for the different types of complexities affecting the data while accommodating a sensitivity analysis to explore the impact of alternative missingness assumptions on the inferences and on the decision-making process for health technology assessmentWith Michael Daniels and Gianluca Baio
We review the evolution of health economic evaluation practice in the Netherlands before and after the introduction of the ZIN’s 2016 guidelines. Based on some key components within the health economics framework addressed by the new guidelines, we specifically focus on reviewing the statistical methods, missing data methods and software implemented by health economists.
We extend the current methods for modelling trial-based partitioned survival cost-utility data, taking advantage of the flexibility of the Bayesian approach, and specify a joint probabilistic model for the health economic outcomes. This allows to account for multiple types of data complexities and the dependence relationships between different types of quality of life and cost components.
We extend and adapt the modelling framework typically used for the analysis of football data for the analysis and prediction of volleyball results in regular seasons. Three different sub-models (points scored, number of sets, winner) which are jointly modelled using a flexible Bayesian parametric approach, which allows to fully propagate the uncertainty for each unobserved quantity and to assess the predictive performance of the model.
The primary role of economic evaluation for health technology assessment (HTA) is not the estimation of the quantities of interest but to aid decision making. Thus, standard frequentist analyses, who rely on power calculations and p-values, are not well-suited for addressing these HTA requirements. Rather, HTA should embrace a decision-theoretic paradigm to inform two decisionsWith Andrea Manca and Gianluca Baio
Our proposed modelling framework allows jointly tackling of the different complexities that affect the data in a relatively easy way by means of its modular structure and flexible choice for the distributions of the QALYs and cost variables. We specifically focus on appropriately modelling spikes at the boundary and missingness, as they have substantial implications in terms of cost-effectiveness results.With Alexina Mason and Gianluca Baio
