2018
DOI: 10.1002/sim.8045
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A full Bayesian model to handle structural ones and missingness in economic evaluations from individual‐level data

Abstract: Funding informationThe Foundation BLANCEFLOR Boncompagni Ludovisi, née Bildt ; Mapi Group Economic evaluations from individual-level data are an important component of the process of technology appraisal, with a view to informing resource allocation decisions. A critical problem in these analyses is that both effectiveness and cost data typically present some complexity (eg, nonnormality, spikes, and missingness) that should be addressed using appropriate methods. However, in routine analyses, standardised app… Show more

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Cited by 25 publications
(35 citation statements)
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“…Additionally, missing data was assumed to be MAR, however, this assumption might not always hold and data could be MNAR. Recently, an increasing number of guidelines and studies emphasize the importance of checking for possible departure from the MAR assumption [28,36,[75][76][77]. It is recommended to perform sensitivity analyses, using other methods such as selection and/or patternmixture models [76].…”
Section: Comparison To Other Studies and Implications For Further Resmentioning
confidence: 99%
“…Additionally, missing data was assumed to be MAR, however, this assumption might not always hold and data could be MNAR. Recently, an increasing number of guidelines and studies emphasize the importance of checking for possible departure from the MAR assumption [28,36,[75][76][77]. It is recommended to perform sensitivity analyses, using other methods such as selection and/or patternmixture models [76].…”
Section: Comparison To Other Studies and Implications For Further Resmentioning
confidence: 99%
“…In principle BPA allows to fit the model of interest while simultaneously handling all the different issues related to the missing data as well as to correctly propagate and quantify uncertainty. Each unobserved quantity in the model is handled as if it were a parameter (17,(42)(43)(44). Costs and QALYS were estimated through a joint distribution based on Markov Chain Monte Carlo (MCMC) using the R function selection, within the missingHE package (17).…”
Section: Bayesian Parametric Approach (Bpa)mentioning
confidence: 99%
“…The generation of synthetic datasets is a fundamentally Bayesian operation, as is MI. 27,48,49 Both can be regarded as fully Bayesian approaches; with a few posterior samples making up the imputed datasets. In PAIC, we can use efficient sampling methods such as Markov Chain Monte Carlo (MCMC) in this stage.…”
Section: Generation Of Synthetic Datasetsmentioning
confidence: 99%
“…In standard multiple imputation, the imputation and analysis stages may be performed simultaneously in a joint model. 49 However, this is problematic in PAIC as the dependent variable * of the analysis is completely synthesized. Consider the DAG in Figure 1.…”
Section: Analysis Of Synthetic Data Setsmentioning
confidence: 99%