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screening_Stat205 - Introduction to screening tests Tim...

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Introduction to screening tests Tim Hanson Department of Statistics University of South Carolina April, 2011 1
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Overview: 1. Estimating test accuracy: dichotomous tests. 2. Estimating test accuracy: continuous tests. 3. Does adding additional tests help? 4. Statistical research: modeling populations of nondiseased G 0 and diseased G 1 continuous test responses. 2
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We assume a state loosely termed diseased D + or not diseased D - , but any event of interest works. Examples : D + = cardiovascular disease D + = hepatitis B D + = Parkinson’s disease D + = recent use of illegal drugs Notice shades of gray and differences in these outcomes. Cardiovascular disease is an umbrella term and can be tested for many different ways: exercise stress test, MRI, X-ray, Echocardiogram, CT scan, PET, SPECT, plus various blood tests. Usually diagnosis takes multiple tests into account. Drug use is known to the person being tested! Hepatitis B is either there or not. 3
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Binary tests : result in one of two outcomes, either T + or T - . Examples : over the counter pregnancy tests rapid strep test cultures (either something grows or it doesn’t) direct microscopic examination of body fluid (either see it or not) asking a potential employee if they’ve recently used illegal drugs 4
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Continuous tests : result in a number Y . Typically as the number increases the likelihood of D + increases. Examples : Enzyme-Linked ImmunoSorbent Assay (ELISA) measures an inferred amount of antigen in a blood sample minutes of briskly walking on a treadmill before discomfort pathologist classifying a slide as (1) negative, (2) atypical squamous hyperplasia, (3) carcinoma in situ (not metastasized), (4) invasive carcinoma (metastasized) Often a continuous test is made into a binary one by dichotomizing : T + Y > k and T - ⇔ Y k . 5
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Binary tests An individual from a population will fall into one of four categories: ( D + , T +), ( D + , T - ), ( D - , T +), or ( D - , T - ). These are ‘true positive’, ‘false negative’, ‘false positive’, and ‘true negative’.
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