Using a repeated measure mixed model for handling missing cost and outcome data in clinical trial-based cost-effectiveness analysis
Description
OBJECTIVES: Where trial recruitment is staggered over time, patients may have different lengths of follow-up, meaning that the dataset is an unbalanced panel with a considerable amount of missing data. This study presents a method for estimating the difference in total costs and total Quality – Adjusted Life Years (QALY) over a given time horizon using a repeated measure mixed model (RMMM). To the authors’ knowledge this is the first time this method has been exploited in the context of economic evaluation within clinical trials. METHODS: An example (EVLA trial, NIHR HTA project 11/129/197) is used where patients have between 1 and 5 years of follow up. Early treatment is compared with delayed treatment. Coefficients at each time point from the repeated measures mixed model were aggregated to estimate total mean cost and total mean QALY over 3 years. Results were compared with other methods for handling missing data: Complete-Case-Analysis (CCA), multiple imputation using linear regression (MILR) and using predictive mean matching (MIPMM), and Bayesian parametric approach (BPA). RESULTS: Mean differences in costs obtained varied among the different approach, CCA, MIPMM and MILRM recorded greater mean costs in delayed treatment, £216 (95% CI -£1413 to £1845), £36 (95% CI to £-581 to 652£), £30(95% CI to -£617 to 679£), respectively. While RMM and BPA showed greater costs in early intervention, -£67 (95% CI -£1069 to £855), -£162 (95% CI -£728-£402), respectively. Early intervention was associated with greater QALY among all methods at year 3, RMM show the highest QALYs, 0.073 (95% CI -0.06 to 0.2). CONCLUSION: MIPMM show most efficient results in our cost-effectiveness analysis. By contrast when the percentage of missing is high RMM shows similar results than MIPMM. Hence, we conclude that RMM is a flexible way and robust alternative for modelling continuous outcomes data that can be considered missing-at-random.