Abstract
Bayes’ theorem is 3 centuries old, but its time has finally arrived. Although Bayesian methods could be helpful for probability estimates of key clinical status (eg, diagnosis, treatment responder, risk of dropout, or self-harm), they have been slow to permeate psychological research, training, or practice. Improvements in technology make it feasible to gather more data from clients, score it in real time, and feed it into Bayesian algorithms. These methods have transformed weather forecasting, prediction of elections and sporting events, and now medicine. Supercomputers are using these tools to build dashboards to integrate information and guide care, and evidence-based medicine has developed a range of low-tech tools (eg, probability nomograms) and supporting software. Evidence-based assessment in psychology melds these methods with traditional strengths of psychological assessment. This talk evaluates the clinical use, using pediatric bipolar disorder as a challenging example.
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