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Lecture Signals, systems & inference – Lecture 23: Neyman-Pearson testing. Signal detection

Lecture Signals, systems & inference – Lecture 23: Neyman-Pearson testing. Signal detection. The following will be discussed in this chapter: Likelihood ratio test (LRT) implemention of MAP rule, terminology, testing for diabetes, testing for prostate cancer. | Lecture Signals, systems & inference – Lecture 23: Neyman-Pearson testing. Signal detection Neyman-Pearson testing. Signal detection 6.011, Spring 2018 Lec 23 1 Likelihood ratio test (LRT) implemention of MAP rule ‘H1 ’ > p1 .fR|H (r|H1 ) p0 .fR|H (r|H0 ) < ‘H0 ’ ‘H1 ’ fR|H (r|H1 ) > p0 ⇤(r) = =⌘ fR|H (r|H0 ) < p1 ‘H0 ’ 2 Terminology • prevalence (p1) • (conditional ) probability of detection, sensitivity, true positive rate, recall • specificity, true negative rate • (conditional) probability of false alarm, false positive rate (= 1– specificity) • (conditional) probability of a miss, false negative rate (= 1 – sensitivity) • positive predictive value, precision 3 • negative predictive value Testing for diabetes © World Health Organization. All rights reserved. This content is excluded from our Creative Commons license. For more information, see https://ocw.mit.edu/help/faq-fair-use/ 4 Screening for Type 2 Diabetes, WHO 2003 Testing for prostate cancer Courtesy of Elsevier, Inc., https://www.sciencedirect.com. Used with permission. For clinically significant cancer, MP-MRI was more sensitive (93%) than TRUS-biopsy (48%) and less specific (41%) for MP-MRI vs 96% for TRUS-biopsy. 5.9% of 740 patients reported serious adverse events, 5 including 8 cases of sepsis. Ahmed et al., Lancet Feb 2017 MIT OpenCourseWare https://ocw.mit.edu 6.011 Signals, Systems and Inference Spring 2018 For information about citing these materials or our Terms of Use, visit: https://ocw.mit.edu/terms. 6