Purpose: Multiple imputation (MI) is an attractive approach for addressing missing data in cost-effectiveness analyses (CEA). However, to provide appropriate inferences the imputation model must reflect the data’s structure. CEA alongside cluster randomised trials (CRTs), tend to have complex patterns of missing data. Previous studies have ignored the missingness mechanisms and applied complete-case analysis (CCA) or single-level MI. This paper presents multilevel MI approach for CEA alongside CRTs, and compares the results to those from conventional methods.
Method: We compared the relative performance of alternative methods for handling missing data across a wide range of circumstances. We generated different scenarios with missing costs and health outcomes, using a CEA alongside a CRT with fully-observed data. The CRT (4252 patients, 14 clusters) evaluated an intervention to improve diagnosis of active labour in primiparous women. We constructed scenarios that differed, for example, according to the proportion with missing data (e.g. 30%, 50%) and the missingness mechanisms (e.g. Missing Completely at Random (MCAR) or Missing at Random (MAR)). We estimated incremental net benefits (INB) with each method, and compared these to the corresponding estimates from the fully-observed data, taken to be the ‘true’ INB.
Result: When costs and outcomes were MCAR, all methods gave INBs similar to the ‘true’ estimates. When endpoints were MAR, the CCA gave estimates which differed from the ‘true’ INBs. Across all these scenarios, the single-level MI provided misleading point estimates and understated the uncertainty surrounding the INBs. Unlike single-level MI, the multilevel MI provided both point estimates and precision consistently close to the ‘true’ values, even in more challenging settings, such as when there were high levels of missing data. For example, when 50% of observations had costs and outcomes MAR, the probabilities that the intervention was cost-effective were 0.55 [CCA], 0.50 [single-level MI], 0.40 [multilevel MI], compared to the ‘true’ estimate of 0.39.
Conclusion: MI methods can appropriately handle missing data in CEA, but it is fundamental that the imputation model recognises the structure of the cost-effectiveness data. In CEA that use CRTs, MI can only provide appropriate inferences if the approach reflects the inherent clustering.