Quantifying diagnostic uncertainty using item response theory: the Posterior Probability of Diagnosis Index

Using traditional Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (American Psychiatric Association, 2000) diagnostic criteria, clinicians are forced to make categorical decisions (diagnosis vs. no diagnosis). This forced choice implies that mental and behavioral health disorders are categorical and does not fully characterize varying degrees of uncertainty associated with a particular diagnosis. Using an item response theory (latent trait model) framework, we describe the development of the Posterior Probability of Diagnosis (PPOD) Index, which answers the question: What is the likelihood that a patient meets or exceeds the latent trait threshold for a diagnosis? The PPOD Index is based on the posterior distribution of θ (latent trait score) for each patient's profile of symptoms. The PPOD Index allows clinicians to quantify and communicate the degree of uncertainty associated with each diagnosis in probabilistic terms. We illustrate the advantages of the PPOD Index in a clinical sample (N = 321) of children and adolescents with oppositional defiant disorder.