Comparing Methods for Handling Missing Cost and Outcome Data in Clinical Trial-based Cost-effectiveness Analysis



OBJECTIVES: This study compares methods for handling missing data to conduct cost-effectiveness analysis in the context of a clinical study.

METHODS: A long-term clinical trial with staggered recruitment was used as a case study (EVRA, NIHR-HTA project 11/129/197). Patients had between 1 year and 5.5 years (median 3 years) of follow-up under “early” or “deferred” treatment. The methods compared were Complete-Case-Analysis (CCA), multiple imputation using linear regression (MILR) and using predictive mean matching (MIPMM), Bayesian parametric approach using the R package missingHE (BPA) and repeated measures mixed model (RMM). The outcomes were total mean costs and total mean quality-adjusted life years (QALYs) at different time horizons (1 year, 3 years and 5 years).

RESULTS: All methods found no statistically significant difference in cost at the 5% level in all years, and all methods found statistically significantly greater mean QALY at year 1. At year 3, BPA showed a statistically significant difference while other method did not at later time horizons. Standard errors differed substantially between the methods employed.

CONCLUSION:  MIPMM seemed to perform better than MILM, confirming findings from simulation studies. RMM and BPA might be feasible options, though they did not perform better than MIPMM in this dataset. Further simulation studies and applications should continue to compare these methods.

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