11/9/2022 0 Comments Cnu spss codeThese methods also provide a statistical process for comparing different tests and deciding whether one is superior for making these classification decisions. ROC and related methods are the engine for statistically appraising a test’s performance at classifying cases into groups correctly-such as those with versus without mood disorder. These constitute important foreground and background for the role of ROC, guiding decisions about which assessment methods are contenders for clinical use, and how to implement them in practice. There have been recent advances in guidelines and recommendations for STAndardized Reporting of tests of Diagnostic assessments (the STARD Guidelines Bossuyt et al., 2003) and tools to help critically evaluate reporting of results ( Whiting et al., 2011) there are excellent treatments of how to apply Bayesian methods to clinical decision making within an evidence-based medicine (EBM) framework ( Straus, Glasziou, Richardson, & Haynes, 2011). Table I lays out a larger context of where ROC and related methods fit in a fully developed program that moves from basic assessment research to clinical decisions with an individual patient. The primer provides guidance about making informed choices of cut scores, and then packaging the findings in a way that promotes clinical decision making. Graphical methods play a central role in the ROC approach to evaluating tests. The primer compares and contrasts traditional ways of assessing criterion validity versus ROC, and illustrates methods for checking assumptions, running the main analyses, and generating figures. This primer illustrates the application of receiver operating characteristic (ROC) analysis and related diagnostic efficiency statistics to a research data set, using two popular statistical programs, SPSS and R, to run the analyses. Signal detection theory ( McFall & Treat, 1999 Swets, Dawes, & Monahan, 2000) and Bayesian methods ( Bayes & Price, 1763 Kruschke, 2011) provide a statistical and conceptual framework for taking the research data and translating them into direct answers to these practical clinical questions. But what does the score mean in the context of this individual patient? We know that youths with depression tend to score higher, on average, on these scales, but can we translate her score into an estimate of the probability that this girl has depression? What should we do next … more assessment? Refer for treatment for depression? The CBCL is widely used in clinical settings and in research, and it has accumulated evidence of validity for diagnoses of depression ( Warnick, Bracken, & Kasl, 2008). She earns a raw score of 27 on Internalizing problems, and a 7 on a Diagnostic and Statistical Manual of Mental Disorders (DSM)-oriented Affective Problems scale, and a T-score of 76, based on comparing her score with other girls in her age range in the standardization sample. Her parents complete the standard paperwork, including an Achenbach Child Behavior Checklist (CBCL Achenbach & Rescorla, 2001), which our clinic uses as a brief, broad measure to identify if there are any emotional or behavioral concerns that might complicate treatment or warrant intervention in their own right. Appendices include sample scripts using SPSS and R to check assumptions and conduct ROC analyses.Ī 10-year-old girl comes to our medical clinic for a psychological evaluation assessing factors that might contribute to problems adhering to her diabetes management regimen. The ROC framework offers important advantages for clinical interpretation. Conclusions This study illustrates a series of steps in defining a clinical problem, operationalizing it, selecting a valid study design, and using ROC analyses to generate statistics that support clinical decisions. Internalizing scores 30 had a diagnostic likelihood ratio of 7.4. Results Internalizing Problems raw scores discriminated mood disorders significantly better than did age- and gender-normed T scores, or an Affective Problems score. Method Secondary analyses of data from 589 families seeking outpatient mental health services, completing the Child Behavior Checklist and semi-structured diagnostic interviews. Objective To offer a practical demonstration of receiver operating characteristic (ROC) analyses, diagnostic efficiency statistics, and their application to clinical decision making using a popular parent checklist to assess for potential mood disorder.
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