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

Abstract

Background Baseline imbalances, skewed costs, the correlation between costs and effects, and missing data are statistical challenges that are often not adequately accounted for in the analysis of cost-effectiveness data. This study aims to illustrate the impact of accounting for these statistical challenges in trial-based economic evaluations. Methods Data from two trial-based economic evaluations, the REALISE and HypoAware studies, were used. In total, 14 full cost-effectiveness analyses were performed per study, in which the four statistical challenges in trial-based economic evaluations were taken into account step-by-step. Statistical approaches were compared in terms of the resulting cost and effect differences, ICERs, and probabilities of cost-effectiveness. Results In the REALISE study and HypoAware study, the ICER ranged from 636,744€/QALY and 90,989€/QALY when ignoring all statistical challenges to − 7502€/QALY and 46,592€/QALY when accounting for all statistical challenges, respectively. The probabilities of the intervention being cost-effective at 0€/ QALY gained were 0.67 and 0.59 when ignoring all statistical challenges, and 0.54 and 0.27 when all of the statistical challenges were taken into account for the REALISE study and HypoAware study, respectively. Conclusions Not accounting for baseline imbalances, skewed costs, correlated costs and effects, and missing data in trial-based economic evaluations may notably impact results. Therefore, when conducting trial-based economic evaluations, it is important to align the statistical approach with the identified statistical challenges in cost-effectiveness data. To facilitate researchers in handling statistical challenges in trial-based economic evaluations, software code is provided.

The economic burden of knee and hip osteoarthritis: absenteeism and costs in the Dutch workforce

“…The lack of recent and high-quality research on the topic of indirect costs of OA in combination with methodological heterogeneity makes the comparison with literature difficult [ 3 , 5 – 7 ]. Salmon et al reviewed papers measuring the economic burden of OA worldwide and recalculated them to fit the societal perspective per patient per year [ 20 ].…”

“…The lack of evidence on indirect costs possibly prevails because there is debate regarding the best method to measure productivity costs (e.g. cost calculation approach, and handling of skewed and missing data), and because data on productivity are often difficult to obtain [ 3 , 6 , 7 ]. In the Netherlands no reliable national registries for sick leave and absenteeism data are available.…”

Economic evaluations in occupational health: what brings the best bang for the buck?

“…The statistical quality of many economic evaluations in occupational health is poor (9,14). Amongst others, baseline imbalances, the skewed nature of cost data, and the clustering of data are often neglected.…”

“…On top of that, missing data are frequently handled using inappropriate methods, such as a complete-case analysis, and some studies do not even report on the uncertainty surrounding their cost-effectiveness and/or return-on-investment estimates (8). Inappropriate statistical methods, however, can lead to incorrect results and/or conclusions, and hence a sub-optimal allocation of available occupational health resources (9,14). Improving the statistical quality of economic evaluations in occupational health starts with the development of recommendations on how to analyze them appropriately, accompanied by the publication of tutorial papers and annotated software codes.…”

“…Improving the statistical quality of economic evaluations in occupational health starts with the development of recommendations on how to analyze them appropriately, accompanied by the publication of tutorial papers and annotated software codes. Also, as extensive progress has been made concerning the statistical analysis of economic evaluations in other research fields [eg, (14)(15)(16)], the quality of economic evaluations in occupational health will also benefit from an increased collaboration with health economists, biostatisticians, and/or econometricians. In the present issue, Finnes et al (5) provide a good example of such a collaboration, which in turn resulted in a state-of-the-art economic evaluation from the occupational health as well as economic perspective.…”