Taking the Analysis of Trial-Based Economic Evaluations to the Next Level: The Importance of Accounting for Clustering

Abstract

Objectives The aim of this study was to assess the performance and impact of multilevel modelling (MLM) compared with ordinary least squares (OLS) regression in trial-based economic evaluations with clustered data. Methods Three thousand datasets with balanced and unbalanced clusters were simulated with correlation coefficients between costs and effects of − 0.5, 0, and 0.5, and intraclass correlation coefficients (ICCs) varying between 0.05 and 0.30. Each scenario was analyzed using both MLM and OLS. Statistical uncertainty around MLM and OLS estimates was estimated using bootstrapping. Performance measures were estimated and compared between approaches, including bias, root mean squared error (RMSE) and coverage probability. Cost and effect differences, and their corresponding confidence intervals and standard errors, incremental cost-effectiveness ratios, incremental net-monetary benefits and cost-effectiveness acceptability curves were compared. Results Cost-effectiveness outcomes were similar between OLS and MLM. MLM produced larger statistical uncertainty and coverage probabilities closer to nominal levels than OLS. The higher the ICC, the larger the effect on statistical uncertainty between MLM and OLS. Significant cost-effectiveness outcomes as estimated by OLS became non-significant when estimated by MLM. At all ICCs, MLM resulted in lower probabilities of cost effectiveness than OLS, and this difference became larger with increasing ICCs. Performance measures and cost-effectiveness outcomes were similar across scenarios with varying correlation coefficients between costs and effects. Conclusions Although OLS produced similar cost-effectiveness outcomes, it substantially underestimated the amount of variation in the data compared with MLM. To prevent suboptimal conclusions and a possible waste of scarce resources, it is important to use MLM in trial-based economic evaluations when data are clustered.

The statistical approach in trial-based economic evaluations matters: get your statistics together!

“…It is recommended to perform sensitivity analyses, using other methods such as selection and/or pattern-mixture models [77]. Furthermore, the handling of clustered data or longitudinal data was not investigated in this study, whereas failure to account for clustering will underestimate statistical uncertainty, can lead to inaccurate point estimates, and may in turn lead to incorrect inferences [10,71,79]. It is also important to note that the statistical challenges identified in this study are only a selection of possible statistical issues that might arise when analyzing trial-based economic evaluations [51].…”

Health Economic Evaluation Alongside Stepped Wedge Trials: A Methodological Systematic Review

“…More recently another simulation study has shown that failing to take into account the clustered structure of the data in trial based economic evaluations using for instance ordinary least squares regression rather than MLMs leads to a substantial underestimation of the amount of variation [89]. We would also like to highlight that authors have seldomly specified the term bivariate (mixed/multilevel) model to explicitly acknowledge the (potential) correlation between costs and effectiveness data.…”

Stratified care integrated with eHealth versus usual primary care physiotherapy in patients with neck and/or shoulder complaints: protocol for a cluster randomized controlled trial

“…The results of the imputed datasets will be pooled using Rubin's rules [62]. LMM, with the same three-level structure as described above, will be performed to estimate cost and effect differences [76]. In order to account for the highly skewed nature of cost data, bias-corrected and accelerated bootstrapping with 5000 replications will be used to estimate 95% confidence intervals around the cost differences.…”