Using dual-criteria methods to supplement visual inspection: Replication and extension

The dual-criteria and conservative dual-criteria methods effectively supplement visual analysis with both simulated and published datasets. However, extant research evaluating the probability of observing false positive outcomes with published data may be affected by case selection bias and publication bias. Thus, the probability of obtaining false positive outcomes using these methods with data collected in the course of clinical care is unknown. We extracted baseline data from clinical datasets using a consecutive controlled case-series design and calculated the proportion of false positive outcomes for baseline phases of various lengths. Results replicated previous findings from Lanovaz, Huxley, and Dufour (2017), as the proportion of false positive outcomes generally decreased as the number of points in Phase B (but not Phase A) increased using both methods. Extending these findings, results also revealed differences in the rate of false positive outcomes across different types of baselines.