onco-stats

Screening Tests: Quiz Questions

Question 1

Which scenario describes a test that is Accurate but not Precise?

A. Readings: 9.8, 9.9, 9.7 (True value: 9.81)

B. Readings: 10.5, 10.6, 10.7 (True value: 10.0)

C. Readings: 9.0, 10.0, 11.0 (True value: 10.0)

D. Readings: 9.9, 10.1, 10.0 (True value: 10.0)

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Answer: C

Explanation:

Accuracy refers to how close measurements are to the true value (absence of systematic error)
Precision refers to how close measurements are to each other (absence of random error)

Let’s evaluate each option:

A: Mean = 9.8, True = 9.81 → Accurate AND Precise (measurements clustered close to true value)
B: Mean = 10.6, True = 10.0 → NOT Accurate but Precise (measurements clustered but away from true value)
C: Mean = 10.0, True = 10.0 → Accurate but NOT Precise (measurements spread out but average equals true value)
D: Mean = 10.0, True = 10.0 → Accurate AND Precise (measurements clustered close to true value)

Option C shows readings that average to the true value (accurate) but are widely spread (not precise).

Question 2

Which type of error primarily affects the accuracy of measurements?

A. Random error

B. Systematic error

C. Human error

D. Instrumental error

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Answer: B

Explanation:

Systematic error (bias) causes measurements to deviate consistently in one direction from the true value, thus reducing accuracy
Random error causes measurements to scatter around the true value, thus reducing precision but not accuracy

Key relationships to remember:

Error Type	Affects	Result
Systematic error	Accuracy	Measurements shifted away from true value
Random error	Precision	Measurements scattered around the mean

Human error and instrumental error can contribute to either systematic or random error depending on their nature.

Question 3

A simple routine test for the presence of HIV was carried out on 300 high risk subjects (intravenous drug users). A more accurate ‘gold standard’ test was also carried out on the subjects to assess the accuracy of the routine test. The following results were obtained:

		HIV by ‘gold standard’
		Yes	No	Total
Routine test	+ve	92	10	102
	-ve	2	196	198
	Total	94	206	300

Select all of the following statements which you believe to be true.

A. The sensitivity of the test is 90.2%

B. The specificity of the test is 97.9%

C. The estimated prevalence of HIV in the relevant population is 0.31

D. The positive predictive value of the test is 97.9%

E. The negative predictive value of the test is 99.0%

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Answer: C and E

Explanation:

First, let’s identify the values from the 2×2 table:

True Positives (a) = 92
False Positives (b) = 10
False Negatives (c) = 2
True Negatives (d) = 196

Now calculate each measure:

Sensitivity = a / (a + c) = 92 / 94 = 0.979 = 97.9% → Statement A (90.2%) is FALSE

Specificity = d / (b + d) = 196 / 206 = 0.951 = 95.1% → Statement B (97.9%) is FALSE

Prevalence = (a + c) / N = 94 / 300 = 0.313 ≈ 0.31 → Statement C is TRUE

PPV = a / (a + b) = 92 / 102 = 0.902 = 90.2% → Statement D (97.9%) is FALSE

NPV = d / (c + d) = 196 / 198 = 0.990 = 99.0% → Statement E is TRUE

Note: The values for sensitivity (97.9%) and PPV (90.2%) have been swapped in the incorrect options - a common exam trap!

Question 4

Properties of a diagnostic test

Which of the following statements are true?

A. The sensitivity of a diagnostic test is the proportion of individuals without the disease who are correctly identified by the test.

B. The positive predictive value of a diagnostic test is the proportion of individuals with the disease who are correctly identified by the test.

C. The negative predictive value of a diagnostic test will not change if the prevalence of the condition increases.

D. For a condition which is easily treatable, we should like the diagnostic test to have a high sensitivity.

E. The likelihood ratio for a positive diagnostic test result is the ratio of the chance of a positive result if the patient has the disease to the chance of a positive result if he/she does not have the disease.

Click to reveal answer

Answer: D and E

Explanation:

A. FALSE — This describes specificity, not sensitivity.

Sensitivity = proportion of individuals with the disease who test positive
Specificity = proportion of individuals without the disease who test negative

B. FALSE — This describes sensitivity, not PPV.

PPV = proportion of individuals with a positive test who have the disease
Sensitivity = proportion of individuals with the disease who test positive

C. FALSE — Predictive values are affected by prevalence.

As prevalence increases, PPV increases and NPV decreases
Sensitivity and specificity are independent of prevalence
This is a crucial distinction to remember!

D. TRUE — For easily treatable conditions, we want high sensitivity to ensure we don’t miss cases (minimise false negatives).

High sensitivity = ability to “rule OUT” disease (SnNOut)
Missing a treatable case could deny someone beneficial treatment

E. TRUE — The likelihood ratio for a positive result (LR+) is:

\[LR+ = \frac{sensitivity}{1 - specificity} = \frac{P(+|disease)}{P(+|no\ disease)}\]

This tells us how much more likely a positive result is in someone with the disease compared to someone without.

Key Formulas Summary

Measure	Formula	Interpretation
Sensitivity	a / (a + c)	Proportion of diseased correctly identified
Specificity	d / (b + d)	Proportion of non-diseased correctly identified
PPV	a / (a + b)	Proportion of positive tests that are true positives
NPV	d / (c + d)	Proportion of negative tests that are true negatives
Prevalence	(a + c) / N	Proportion of population with disease
LR+	sensitivity / (1 - specificity)	How much more likely a positive test is in disease
LR-	(1 - sensitivity) / specificity	How much more likely a negative test is in disease

Source: Screening Tests lecture materials; Medical Statistics at a Glance Workbook, Petrie et al.

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