Imputing missing patient-level data and propensity score matching in cost-effectiveness analysis in Crohn's disease

Abstract

ABSTRACT Objectives The effect of imputing missing data followed by propensity score analysis on the incremental cost-effectiveness ratio (ICER) in a cost-effectiveness analysis is unknown. The objective was to compare alternative approaches in grouping data following imputation and prior to calculating propensity scores for use in economic evaluation. Methods Patient-level data from an observational study of 573 children with Crohn’s disease were used in a microsimulation model to determine the incremental cost of early anti-tumor necrosis factor-α treatment compared to standard care per remission week gained. Multiple imputation of a missing covariate followed by propensity score matching to create comparator groups was approached in two ways. The Within approach calculated propensity scores on each imputed dataset separately, while the Across method averaged propensity scores to create one matched population resulting in multiple sets of health state transition probabilities. Results The incremental cost per remission week gained ranged from CAD$2,236 to CAD$12,464 (mean CAD$4,266) with Within datasets and was CAD$4,679 per remission week gained with the Across dataset. Conclusion Imputation of missing patient-level data and propensity score analysis increases methodological uncertainty in cost-effectiveness analysis. The present study indicated that the Across approach may be less cumbersome, and slightly reduce bias and variance.