Impact of the "Linked Evidence Approach" Method on Policies to Publicly Fund Diagnostic, Staging, and Screening Medical Tests. Issue 1 (October 2016) - Merlin, Tracy L.; Hiller, Janet E.; Ryan, Philip
Background: The linked evidence approach (LEA) is used in health technology assessment (HTA) to evaluate the clinical utility of new medical tests in the absence of direct trial evidence.Objective: To determine whether use of LEA affects decisions to publicly fund medical tests.Methods: Australian HTAs that evaluated medical tests before and after LEA was mandated (in 2005) were screened for eligibility. Data were extracted and the impact of LEA and other possible clinical predictors (selected a priori) on funding decisions was modelled. Regression diagnostics were performed to estimate model fit, model specification, and to inform model selection. The unit of analysis was per clinical indication for each new test, so analyses were adjusted for clustering.Results: 83 HTAs (for 173 clinical indications) were eligible from the 259 screened. When health policy was compared before and after 2005, there was an 11% reduction in overall positive funding decisions, including a 25% decrease in "interim" (coverage with evidence development) funding decisions. The odds of obtaining interim funding reduced by 98% (odds ratio = 0.02, 95% confidence interval = 0.0005, 0.17), but there was no change in the direction of funding decisions (odds ratio = 1.36, 95% confidence interval = 0.62, 3.01). Across both time periods, when LEA was used there was a very strong likelihood that the medical test would not receive interim funding (χ 2 = 12.63, df = 1, P = 0.001). For positive funding decisions, the strongest predictors were whether or not the new test would replace an existing test and whether the available evidence was limited.Conclusions: The use of LEA did not predict the direction of funding decisions. Application of the method did predict that a "coverage with evidence development" decision was unlikely. This suggests that LEA may reduce decision-maker uncertainty.