Diagnostic Test Studies Tran The Trung Nguyen Quang Vinh

Why we need a diagnostic test?  We need “information” to make a decision  “Information” is usually a result from a test  Medical tests: yTo screen for a risk factor (screen test) yTo diagnosse a disease (diagnostic test) yTo estimate a patient’s prognosis (pronostic test)  When and in whom, a test should be done? yWhen “information” from test result have a value.

Value of a diagnostic test  The ideal diagnostic test: yAlways give the right answer: xPositive result in everyone with the disease xNegative result in everyone else yBe quick, safe, simple, painless, reliable & inexpensive  But few, if any, tests are ideal.  Thus there is a need for clinically useful substitutes

Is the test useful ?  Reproducibility (Precision)  Accuracy (compare to “gold standard”)  Feasibility  Effects on clinical decisions  Effects on Outcomes

Determining Usefulness of a Medical Test QuestionPossible DesignsStatistics for Results 1. How reproducible is the test? Studies of: - intra- and inter observer & - intra- and inter laboratory variability Proportion agreement, kappa, coefficient of variance, mean & distribution of differences (avoid correlation coefficient)

Determining Usefulness of a Medical Test QuestionPossible DesignsStatistics for Results 2. How accurate is the test? Cross-sectional, case- control, cohort-type designs in which test result is compared with a “gold standard” Sensitivity, specificity, PV+, PV-, ROC curves, LRs

Determining Usefulness of a Medical Test QuestionPossible Designs Statistics for Results 3. How often do test results affect clinical decisions? Diagnostic yield studies, studies of pre- & post test clinical decision making Proportion abnormal, proportion with discordant results, proportion of tests leading to changes in clinical decisions; cost per abnormal result or per decision change

Determining Usefulness of a Medical Test QuestionPossible Designs Statistics for Results 4. What are the costs, risks, & acceptability of the test? Prospective or retrospective studies Mean cost, proportions experiencing adverse effects, proportions willing to undergo the test

Determining Usefulness of a Medical Test QuestionPossible DesignsStatistics for Results 5. Does doing the test improve clinical outcome, or having adverse effects? Randomized trials, cohort or case-control studies in which the predictor variable is receiving the test & the outcome includes morbidity, mortality, or costs related either to the disease or to its treatment Risk ratios, odd ratios, hazard ratios, number needed to treat, rates and ratios of desirable and undesirable outcomes

Common Issues for Studies of Medical Tests  Spectrum of Disease Severity and Test Results: yDifference between Sample and Population? yAlmost tests do well on very sick and very well people. yThe most difficulty is distinguishing Healthy & early, presymtomatic disease.  Subjects should have a spectrum of disease that reflects the clinical use of the test.

Common Issues for Studies of Medical Tests  Sources of Variation: yBetween patients yObservers’ skill yEquipments => Should sample several different institutions to obtain a generalizable result.

Common Issues for Studies of Medical Tests  Importance of Blinding: (if possible) yMinimize observer bias yEx. Ultrasound to diagnose appendicitis (It is different to clinical practice)

Studies of Diagnostic tests  Studies of Test Reproducibility  Studies of The Accuracy of Tests  Studies of The Effect of Test Results on Clinical Decisions  Studies of Feasibility, Costs, and Risks of Tests  Studies of The Effect of Testing on Outcomes

Studies of Test Reproducibility  The test is to test the precision yIntra-observer variability yInter-observer variability  Design: yCross-sectional design yCategorical variables: Kappa yContinuous variables: coefficient of variance  Compare to it-self (“gold standard” is not required)

Studies of the Accuracy of Tests  Does the test give the right answer?  “Tests” in clinical practice: ySymptoms ySigns yLaboratory tests yImagine tests  To find the right answer.  “Gold standard” is required

How accurate is the test?  Validating tests against a gold standard:  New tests should be validated by comparison against an established gold standard in an appropriate subjects  Diagnostic tests are seldom 100% accurate (false positives and false negatives will occur)

Validating tests against a gold standard  A test is valid if: yIt detects most people with disorder (high Sen) yIt excludes most people without disorder (high Sp) ya positive test usually indicates that the disorder is present (high PV+)  The best measure of the usefulness of a test is the LR: how much more likely a positive test is to be found in someone with, as opposed to without, the disorder

A Pitfall of Diagnostic test A test can separate the very sick from the very healthy does not mean that it will be useful in distinguish patients with mild cases of the disease from others with similar symptoms

Sampling  The spectrum of patients should be representative of patients in real practice.  Example: Which is better? What is the limits? yChest X-ray to diagnose aortic aneurism (AA). Sample are 100 patients with and 100 without AA that ascertained by CT scan or MRI. yFNA to diagnose thyroid cancer. 100 patients with nodule > 3cm and had indication to thyroidectomy (biopsy was the gold standard).

“Gold standard”  “Gold standard” test: often confirm the presence or absence of the disease : D(+) or D(-).  Properties of “Gold standard”: yRuling in the disease (often doing well) yRuling out the disease (maybe not doing well) yFeasible & ethical ? (ex. Biopsy of breast mass) yWidely acceptable.

The test result  Categorical variable: yResult: Positive or Negative yEx. FNA cytology  Continuous variable: yNext step is: find out “cut-off point” by ROC curve yEx. almost biochemical test: pro-BNP, TR-Ab,..

Analysis of Diagnostic Tests  Sensitivity & Specificity  Likelihood ratio: LR (+), LR (-)  Posterior probability (Post-test probability) / Positive, Negative Predictive value (PPV, NPV); given Prior probability (Pre-test probability) How accurate is the test?

Sensitivity and Specificity Test Result Disease D “Gold standard” +- +ab -cd

Positive & Negative Predictive Value  PV (+): positive predictive value  PV (-): negative predictive value Test Result Disease D +- +ab -cd

Posterior odds When combined with information on the prior probability of a disease*, LRs can be used to determine the predictive value of a particular test result: Posterior odds = Prior odds x Likelihood ratio *expressing the prior probability [p] of a disease as the prior odds [p/(1 ‑ p)] of that disease. Conversely, if the odds of a disease are x/y, the probability of the disease is x / (x + y)

Choice of a cut-off point for continuous results Consider the implications of the two possible errors:  If false ‑ positive results must be avoided (such as the test result being used to determine whether a patient undergoes dangerous surgery), then the cutoff point might be set to maximize the test's specificity  If false ‑ negative results must be avoided (as with screening for neonatal phenylketonuria), then the cutoff should be set to ensure a high test sensitivity

Choice of a cut-off point for continuous results  Using receiver operator characteristic (ROC) curves: ySelects several cut-off points, and determines the sensitivity and specificity at each point yThen, graphs sensitivity (true ‑ positive rate) as a function of 1 ‑ specificity (false ‑ positive rate)  Usually, the best cut-off point is where the ROC curve "turns the corner”

RECEIVER OPERATING CHARACTERISTIC (ROC) curve  ROC curves (Receiver Operator Characteristic)  Ex. SGPT and Hepatitis 1-Specificity Sensitivity 1 1 SGPT D + D - Sum < > Sum