A practitioner's guide to developing critical appraisal skills: How to understand and interpret frequentist and Bayesian approaches applied to serial and parallel diagnostic testing

Background: Serial and parallel diagnostic testing can improve diagnostic accuracy. Yet their application and interpretation remain underused in clinical practice. Types of Studies Reviewed: The authors synthesized statistical methodology literature on serial and parallel diagnostic testing configurations, comparing frequentist and Bayesian analytical frameworks. The authors used caries diagnosis as an example to illustrate the practical application of these approaches in guiding test selection and result interpretation throughout. Results: Serial testing improves specificity and positive predictive value, making it ideal for confirmatory diagnoses, whereas parallel testing improves sensitivity and negative predictive value, making it ideal for initial screening. The frequentist framework provides population-level estimates of test performance, supporting test validation, regulatory decision making, and large-scale screening strategies. In contrast, the Bayesian approach focuses on individualized inference, allowing clinicians to incorporate prior beliefs on the basis of patient-specific information and update disease posterior probabilities as new information becomes available. Moreover, Bayesian methods allow for flexible combination of individual test results rather than treating multiple diagnostic tests as a single composite measure. Conclusions and Practical Implications: Clinicians should use serial testing for confirmatory diagnosis and parallel testing for screening. Understanding both frequentist population-level benchmarks and Bayesian patient-specific probability updating enables more informed testing strategies and result interpretation.