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

Janet MacNeil Vroomen (), Iris Eekhout, Marcel G. Dijkgraaf, Hein van Hout, Sophia E. de Rooij, Martijn W. Heymans and Judith E. Bosmans
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Janet MacNeil Vroomen: University of Amsterdam
Iris Eekhout: University of Amsterdam
Marcel G. Dijkgraaf: University of Amsterdam
Hein van Hout: University of Amsterdam
Sophia E. de Rooij: University of Amsterdam
Martijn W. Heymans: University of Amsterdam
Judith E. Bosmans: University of Amsterdam

The European Journal of Health Economics, 2016, vol. 17, issue 8, No 3, 939-950

Abstract: 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 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10198-015-0734-5

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