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

John Michael Falligant¹, Molly K McNulty², Nicole L Hausman¹

¹Kennedy Krieger Institute & Johns Hopkins University School of Medicine.

Journal of Applied Behavior Analysis

|December 19, 2019

Summary

The dual-criteria and conservative dual-criteria methods show fewer false positives in clinical data as Phase B length increases. Different baseline types also impact false positive rates, offering crucial insights for data analysis.

Keywords:

consecutive controlled case-series dual-criteria method false positives type I error

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Area of Science:

Behavior Analysis
Research Methodology

Background:

Dual-criteria and conservative dual-criteria methods supplement visual analysis but may be biased by existing research limitations.
The false positive rate of these methods with real-world clinical data remains unexamined.

Purpose of the Study:

To evaluate the probability of false positive outcomes using dual-criteria methods with clinical care data.
To investigate the influence of baseline phase length and type on false positive rates.

Main Methods:

A consecutive controlled case-series design was employed to extract baseline data from clinical datasets.
The proportion of false positive outcomes was calculated for varying baseline phase lengths (Phase A and Phase B).

Main Results:

False positive outcome proportions decreased as the number of data points in Phase B increased, consistent with prior research.
The rate of false positive outcomes varied across different types of baselines, indicating method sensitivity to data characteristics.

Conclusions:

The findings support the utility of dual-criteria methods in clinical settings, with recommendations for optimizing baseline phase length.
Understanding baseline characteristics is crucial for accurate interpretation of results derived from these statistical methods.