Cluster-Randomized Test-Negative Design Trials: A Novel and Efficient Method to Assess the Efficacy of Community-Level Dengue Interventions

Abstract

Cluster-randomized controlled trials are the gold standard for assessing efficacy of community-level interventions, such as vector-control strategies against dengue. We describe a novel cluster-randomized trial methodology with a test-negative design (CR-TND), which offers advantages over traditional approaches. This method uses outcome-based sampling of patients presenting with a syndrome consistent with the disease of interest, who are subsequently classified as test-positive cases or test-negative controls on the basis of diagnostic testing. We used simulations of a cluster trial to demonstrate validity of efficacy estimates under the test-negative approach. We demonstrated that, provided study arms are balanced for both test-negative and test-positive illness at baseline and that other test-negative design assumptions are met, the efficacy estimates closely match true efficacy. Analytical considerations for an odds ratio-based effect estimate arising from clustered data and potential approaches to analysis are also discussed briefly. We concluded that application of the test-negative design to certain cluster-randomized trials could increase their efficiency and ease of implementation.

Figure 1.

Validity of odds ratio estimates from a simulated cluster-randomized test-negative design study, under the null hypothesis of no intervention effect. Box-and-whisker plots show the distribution of odds ratio estimates from 1,000 simulated cluster-randomized allocations of a hypothetical dengue preventive intervention, displayed as the % deviation from the expected odds ratio = 1, assuming that the true intervention efficacy is zero. The 10 different scenarios within each graph represent a variable number of clusters under study (20–100) and 2 scenarios of the intercluster coefficient of variation (k) in baseline test-negative illness incidence: high (H; k = 0.5) or low (L; k = 0.25). Intercluster variation in baseline dengue incidence was constant in all scenarios (k = 0.5). Random allocation of the intervention was either unconstrained (A) or constrained to ensure balance between the study arms (within 10%) in baseline dengue incidence (B), test-negative illness incidence (C), or both dengue and test-negative illness incidence (D). Note: 5 odds ratio estimates from panel A (4/1,000 simulations with 20 clusters and high k, and 1/1,000 simulation with 20 clusters and low k) and 2 odds ratio estimates in panel C (2/1,000 simulations with 20 clusters and low k) had a deviation value greater than 150% and are not shown on the graph.