Reference‐based multiple imputation for missing data sensitivity analyses in trial‐based cost‐effectiveness analysis

Leurent, Baptiste; Gomes, Manuel; Cro, Suzie; Wiles, Nicola; Carpenter, James

doi:10.1002/hec.3963

Cited by 13 publications

(18 citation statements)

References 65 publications

(127 reference 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 [12,31,42,77,78]. It is recommended to perform sensitivity analyses, using other methods such as selection and/or pattern-mixture models [77].…”

Section: Comparison To Other Studies and Implications For Further Research And Practicementioning

confidence: 99%

“…Recently, an increasing number of guidelines and studies emphasize the importance of checking for possible departure from the MAR assumption [12,31,42,77,78]. It is recommended to perform sensitivity analyses, using other methods such as selection and/or pattern-mixture models [77]. Furthermore, the handling of clustered data or longitudinal data was not investigated in this study, whereas failure to account for clustering will underestimate statistical uncertainty, can lead to inaccurate point estimates, and may in turn lead to incorrect inferences [10,71,79].…”

Section: Comparison To Other Studies and Implications For Further Research And Practicementioning

confidence: 99%

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The statistical approach in trial-based economic evaluations matters: get your statistics together!

Mutubuki

Alili

Bosmans

et al. 2021

BMC Health Serv Res

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Background Baseline imbalances, skewed costs, the correlation between costs and effects, and missing data are statistical challenges that are often not adequately accounted for in the analysis of cost-effectiveness data. This study aims to illustrate the impact of accounting for these statistical challenges in trial-based economic evaluations. Methods Data from two trial-based economic evaluations, the REALISE and HypoAware studies, were used. In total, 14 full cost-effectiveness analyses were performed per study, in which the four statistical challenges in trial-based economic evaluations were taken into account step-by-step. Statistical approaches were compared in terms of the resulting cost and effect differences, ICERs, and probabilities of cost-effectiveness. Results In the REALISE study and HypoAware study, the ICER ranged from 636,744€/QALY and 90,989€/QALY when ignoring all statistical challenges to − 7502€/QALY and 46,592€/QALY when accounting for all statistical challenges, respectively. The probabilities of the intervention being cost-effective at 0€/ QALY gained were 0.67 and 0.59 when ignoring all statistical challenges, and 0.54 and 0.27 when all of the statistical challenges were taken into account for the REALISE study and HypoAware study, respectively. Conclusions Not accounting for baseline imbalances, skewed costs, correlated costs and effects, and missing data in trial-based economic evaluations may notably impact results. Therefore, when conducting trial-based economic evaluations, it is important to align the statistical approach with the identified statistical challenges in cost-effectiveness data. To facilitate researchers in handling statistical challenges in trial-based economic evaluations, software code is provided.

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Section: Comparison To Other Studies and Implications For Further Research And Practicementioning

confidence: 99%

Section: Comparison To Other Studies and Implications For Further Research And Practicementioning

confidence: 99%

The statistical approach in trial-based economic evaluations matters: get your statistics together!

Mutubuki

Alili

Bosmans

et al. 2021

BMC Health Serv Res

View full text Add to dashboard Cite

show abstract

“…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%

“…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]. Furthermore, the handling of clustered data or longitudinal data was not investigated in this study, whereas failure to account for clustering will underestimate statistical uncertainty, can lead to inaccurate point estimates, and may in turn lead to incorrect inferences [10,78,79].…”

Section: Comparison To Other Studies and Implications For Further Research And Practicementioning

confidence: 99%

The Statistical Approach in Trial-based Economic Evaluations Matters: Get your Statistics Together!

Mutubuki

Alili

Bosmans

et al. 2021

Preprint

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Background: Baseline imbalances, skewed costs, the correlation between costs and effects, and missing data are statistical challenges that are often not adequately accounted for in the analysis of cost-effectiveness data. This study aims to illustrate the impact of accounting for these statistical challenges in trial-based economic evaluations. Methods: Data from two trial-based economic evaluations, the REALISE and HypoAware studies, were used. In total, 14 full cost-effectiveness analyses were performed per study, in which the four statistical challenges in trial-based economic evaluations were taken into account step-by-step. Statistical approaches were compared in terms of the resulting cost and effect differences, ICERs, and probabilities of cost-effectiveness. Results: In the REALISE study and HypoAware study, the ICER ranged from 636,744€/QALY and 90,989€/QALY when ignoring all statistical challenges to -7,502€/QALY and 46,592€/QALY when accounting for all statistical challenges, respectively. The probabilities of the intervention being cost-effective at 0€/ QALY gained were 0.67 and 0.59 when ignoring all statistical challenges, and 0.54 and 0.27 when all of the statistical challenges were taken into account for the REALISE study and HypoAware study, respectively.Conclusions: Not accounting for baseline imbalances, skewed costs, correlated costs and effects, and missing data in trial-based economic evaluations may notably impact results. Therefore, when conducting trial-based economic evaluations, it is important to align the statistical approach with the identified statistical challenges in cost-effectiveness data. To facilitate researchers in handling statistical challenges in trial-based economic evaluations, software code is provided.

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“…CEA often assume that the missingness only depends on the observed data, in that the data are “missing at random” (MAR) (Gabrio et al., 2017; Leurent et al., 2018a). However, in many settings, missing data may depend on outcomes that are unobserved, for example, the patients' health status, and it is more reasonable to assume the data are “missing not at random” (MNAR) (Leurent et al., 2020; Mason et al., 2018). The “true” underlying missing data mechanism cannot be verified from the data at hand, and hence CEA are recommended to report sensitivity analyses according to alternative assumptions about missing data (Faria et al., 2014; Leurent et al., 2018b, 2020; Mason et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Flexible Bayesian longitudinal models for cost‐effectiveness analyses with informative missing data

et al. 2021

Self Cite

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Cost‐effectiveness analyses (CEA) are recommended to include sensitivity analyses which make a range of contextually plausible assumptions about missing data. However, with longitudinal data on, for example, patients' health‐related quality of life (HRQoL), the missingness patterns can be complicated because data are often missing both at specific timepoints (interim missingness) and following loss to follow‐up. Methods to handle these complex missing data patterns have not been developed for CEA, and must recognize that data may be missing not at random, while accommodating both the correlation between costs and health outcomes and the non‐normal distribution of these endpoints. We develop flexible Bayesian longitudinal models that allow the impact of interim missingness and loss to follow‐up to be disentangled. This modeling framework enables studies to undertake sensitivity analyses according to various contextually plausible missing data mechanisms, jointly model costs and outcomes using appropriate distributions, and recognize the correlation among these endpoints over time. We exemplify these models in the REFLUX study in which 52% of participants had HRQoL data missing for at least one timepoint over the 5‐year follow‐up period. We provide guidance for sensitivity analyses and accompanying code to help future studies handle these complex forms of missing data.

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Reference‐based multiple imputation for missing data sensitivity analyses in trial‐based cost‐effectiveness analysis

Cited by 13 publications

References 65 publications

The statistical approach in trial-based economic evaluations matters: get your statistics together!

The statistical approach in trial-based economic evaluations matters: get your statistics together!

The Statistical Approach in Trial-based Economic Evaluations Matters: Get your Statistics Together!

Flexible Bayesian longitudinal models for cost‐effectiveness analyses with informative missing data

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