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Comparative Study
. 2016 Nov;17(8):939-950.
doi: 10.1007/s10198-015-0734-5. Epub 2015 Oct 23.

Multiple imputation strategies for zero-inflated cost data in economic evaluations: which method works best?

Janet MacNeil Vroomen  1 , Iris Eekhout  2 , Marcel G Dijkgraaf  2 , Hein van Hout  2 , Sophia E de Rooij  2 , Martijn W Heymans  2 , Judith E Bosmans  2
Affiliations
Comparative Study

Multiple imputation strategies for zero-inflated cost data in economic evaluations: which method works best?

Janet MacNeil Vroomen et al. Eur J Health Econ. 2016 Nov.

Abstract

Cost and effect data often have missing data because economic evaluations are frequently added onto clinical studies where cost data are rarely the primary outcome. The objective of this article was to investigate which multiple imputation strategy is most appropriate to use for missing cost-effectiveness data in a randomized controlled trial. Three incomplete data sets were generated from a complete reference data set with 17, 35 and 50 % missing data in effects and costs. The strategies evaluated included complete case analysis (CCA), multiple imputation with predictive mean matching (MI-PMM), MI-PMM on log-transformed costs (log MI-PMM), and a two-step MI. Mean cost and effect estimates, standard errors and incremental net benefits were compared with the results of the analyses on the complete reference data set. The CCA, MI-PMM, and the two-step MI strategy diverged from the results for the reference data set when the amount of missing data increased. In contrast, the estimates of the Log MI-PMM strategy remained stable irrespective of the amount of missing data. MI provided better estimates than CCA in all scenarios. With low amounts of missing data the MI strategies appeared equivalent but we recommend using the log MI-PMM with missing data greater than 35 %.

Keywords: Cost data; Economic evaluation; Missing data; Multiple imputation.

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Figures

Fig. 1
Fig. 1
Incremental net benefit (in euros) coefficients for a threshold value of €30,000 based on the amount of missing data and imputation method. MI-PMM multiple imputation with predictive mean matching, log-MI-PMM multiple imputation with predictive mean matching on log-transformed costs, MI-PMM 2 step two-step multiple imputation with predictive mean matching
Fig. 2
Fig. 2
Cost-effectiveness acceptability curves for threshold value of €30,000 based on the 50 % missing data scenario. PMM Multiple imputation with predictive mean matching, Log multiple imputation with predictive mean matching on log-transformed costs, 2step two-step multiple imputation with predictive mean matching
See this image and copyright information in PMC

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