Purpose: This research examines cost-effectiveness analyses (CEAs) with comparable target populations, interventions, and comparators, yet disparate incremental cost-effectiveness ratios (ICERs). The goal of this research is to identify assumptions and parameters used to determine cost-effectiveness, in order to understand underlying differences in CEA outcomes. Methods: From the CEA Registry, we identified three comparative health interventions, in which 11 to 24 CEAs had been conducted for each comparison. These included carotid artery stenting (CAS) vs. carotid endarterectomy (CAE); drug-eluting stents (DES) v. bare-metal stents (BMS); and verenicline (VAR) vs. bupropion (BUP) for smoking cessation therapy. Of the 46 CEAs identified, we reviewed 20 CEAs that used quality-adjusted life-years (QALYs) to represent health effects. For each study, we documented eight parameters to identify potential sources of variability among groups: clinical trial setting, patient randomization, trial duration, time horizon, the inclusion of direct vs. indirect costs, the inclusion of post-intervention costs, study perspective, and sponsorship. For each group, we computed the median ICER and interquartile range, and the percent of CEAs reporting cost-effective outcomes. We used Fischer's exact test to examine the strength of associations between variability parameters and cost-effectiveness. Results: Table 1 presents the median ICER per group (measured by cost per QALY and standardized to US$ 2012), and the percent of studies reporting cost-effective outcomes. The strongest association between study parameters and cost-effectiveness was seen with respect to industry sponsorship: 10 of 12 industry-sponsored studies reported cost-effective outcomes, in comparison to 1 of 7 studies without industry sponsorship (p = 0.003). Outcome variability was also associated with the inclusion vs. exclusion of post-intervention cost data: 11 of 17 analyses that included post-intervention costs reported cost-effective outcomes, in comparison to 0 of 3 studies that included short-term intervention costs only (p = 0.074). 
Conclusions: This research highlights sources of variability in CEA analyses for three comparative health interventions, and the relationships between variability parameters and cost-effectiveness. The data indicate that industry sponsorship significantly influenced ICERs for the interventions examined. The findings from this study provide investigators with insight regarding the interpretation of CEAs with mixed outcomes, despite the use of standard methods for assessing cost-effectiveness.