1. Diagnostic Test Studies
Tran The Trung
Nguyen Quang Vinh

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

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

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

5. Determining Usefulness of a Medical Test
Question
Possible Designs
Statistics 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)

6. Determining Usefulness of a Medical Test
Question
Possible Designs
Statistics 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

7. Determining Usefulness of a Medical Test
Question
Possible 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

8. Determining Usefulness of a Medical Test
Question
Possible 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

9. Determining Usefulness of a Medical Test
Question
Possible Designs
Statistics 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

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

11. Common Issues for Studies of Medical Tests
Sources of Variation:
Between patients
Observers’ skill
Equipments
=> Should sample several different institutions to obtain a generalizable result.

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

13. 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

14. Studies of Test Reproducibility
The test is to test the precision
Intra-observer variability
Inter-observer variability
Design:
Cross-sectional design
Categorical variables: Kappa
Continuous variables: coefficient of variance
Compare to it-self (“gold standard” is not required)

15. Studies of the Accuracy of Tests
Does the test give the right answer?
“Tests” in clinical practice:
Symptoms
Signs
Laboratory tests
Imagine tests
To find the right answer.
“Gold standard” is required

16. 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)

17. Validating tests against a gold standard
A test is valid if:
It detects most people with disorder (high Sen)
It excludes most people without disorder (high Sp)
a 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

18. 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

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

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

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

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

23. Sensitivity and Specificity
Test Result
Disease D
“Gold standard” + - a b c d

24. Positive & Negative Predictive Value
PV (+): positive predictive value
PV (-): negative predictive value
Test Result
Disease D + - a b c d

25. 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)

26. 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

27. Choice of a cut-off point for continuous results
Using receiver operator characteristic (ROC) curves:
Selects several cut-off points, and determines the sensitivity and specificity at each point
Then, 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”

28. RECEIVER OPERATING CHARACTERISTIC (ROC) curve
ROC curves (Receiver Operator Characteristic)
Ex. SGPT and Hepatitis
Sensitivity 1
SGPT
D +
D -
Sum
< 50 10
190
200
50-99 15
135
150 25 65 90 30 60 35 50
120
130
>300 5 70
300
450
750 1
1-Specificity