Biostatistics Practice Questions

Q: The Child Pugh score is a type of what kind of scale?

Ordinal. **Explanation:** The **Child-Pugh Score** is used to assess the prognosis of chronic liver disease (cirrhosis). It is an **Ordinal Scale** because it categorizes patients into groups (Class A, B, or C) based on a numerical score derived from five parameters (Bilirubin, Albumin, INR, Ascites, and Encephalopathy). 1. **Why Ordinal?** An ordinal scale represents data that has a **natural order or rank**, but the mathematical distance between the ranks is not necessarily equal. In the Child-Pugh system, Class A (5–6 points) is "better" than Class B (7–9 points), which is "better" than Class C (10–15 points). Since there is a clear progression of severity, it is ordinal. 2. **Why other options are incorrect:** * **Nominal:** This scale is for naming or labeling categories without any quantitative value or order (e.g., Blood groups A, B, O; Gender). Since Child-Pugh implies a "rank" of severity, it is not nominal. * **Quantitative (Interval/Ratio):** These scales involve precise measurements where the difference between numbers is consistent (e.g., Height in cm, Weight in kg). While the score uses numbers, the "gap" between Class A and B isn't a physical measurement. * **Qualitative:** While ordinal data is a type of categorical (qualitative) data, "Ordinal" is the more specific and correct biostatistical term for ranked data. **High-Yield Clinical Pearls for NEET-PG:** * **Parameters of Child-Pugh (Mnemonic: ABCDE):** **A**lbumin, **B**ilirubin, **C**oagulation (INR), **D**istension (Ascites), **E**ncephalopathy. * **APGAR Score** and **Glasgow Coma Scale (GCS)** are also classic examples of **Ordinal Scales** frequently asked in exams. * **Visual Analogue Scale (VAS)** for pain is another high-yield Ordinal scale.

Q: While testing a hypolipidemic drug, serum lipid levels were tested both before and after its use. Which statistical test is best suited for the analysis of the results?

Paired T test. ### Explanation **Why Paired T-test is the Correct Answer:** The Paired T-test is used to compare the **means of two related groups** (dependent samples). In this scenario, the serum lipid levels are measured in the **same set of individuals** at two different time points: "before" and "after" the intervention. Since each subject acts as their own control, the data points are paired. This test determines if the mean difference between these two observations is statistically significant. **Analysis of Incorrect Options:** * **Student’s T-test (Unpaired/Independent T-test):** This is used to compare the means of two **independent** groups (e.g., comparing lipid levels between Group A taking the drug and Group B taking a placebo). It is incorrect here because the measurements are taken from the same group. * **Chi-square Test:** This is a non-parametric test used for **categorical (qualitative) data** to compare proportions (e.g., "improved" vs. "not improved"). Since serum lipid levels are **quantitative (numerical)** data, the Chi-square test is inappropriate. **Clinical Pearls & High-Yield Facts for NEET-PG:** 1. **Quantitative Data (Means):** * 2 groups (Related/Before-After) → **Paired T-test** * 2 groups (Independent/Different people) → **Unpaired T-test** * >2 groups (Independent) → **ANOVA** (Analysis of Variance) 2. **Qualitative Data (Proportions):** * Comparing two or more proportions → **Chi-square Test** * Small samples (any cell value <5) → **Fisher’s Exact Test** 3. **Key Concept:** Always identify the **type of data** (Numerical vs. Categorical) and the **relationship** (Dependent vs. Independent) before choosing a statistical test.

Q: A diagnostic test was performed. Out of 80 individuals who tested positive, 40 did not have the disease. Out of 9920 individuals who tested negative, 9840 did not have the disease. What is the sensitivity of the test?

33%. ### Explanation To solve this problem, we must first organize the data into a standard **2x2 contingency table**. | | Disease Present (+) | Disease Absent (-) | Total | | :--- | :---: | :---: | :---: | | **Test Positive (+)** | **40 (TP)** | 40 (FP) | 80 | | **Test Negative (-)** | 80 (FN) | **9840 (TN)** | 9920 | | **Total** | 120 | 9880 | 10000 | **Step-by-Step Calculation:** 1. **True Positives (TP):** Total test positives (80) minus False Positives (40) = **40**. 2. **False Negatives (FN):** Total test negatives (9920) minus True Negatives (9840) = **80**. 3. **Sensitivity Formula:** $\frac{TP}{TP + FN} \times 100$ 4. **Calculation:** $\frac{40}{40 + 80} = \frac{40}{120} = \frac{1}{3} \approx \mathbf{33.3\%}$. #### Why the Correct Answer is Right: Sensitivity (True Positive Rate) measures the ability of a test to correctly identify those with the disease. In this cohort, there are 120 diseased individuals, but the test only caught 40 of them, resulting in 33%. #### Why Other Options are Wrong: * **Option A (13%):** Incorrect calculation; likely a result of misplacing values in the 2x2 table. * **Option C (50%):** This is the **Positive Predictive Value (PPV)**: $\frac{TP}{TP+FP} = \frac{40}{80} = 50\%$. * **Option D (99%):** This is the **Specificity**: $\frac{TN}{TN+FP} = \frac{9840}{9840+40} \approx 99.6\%$. #### High-Yield Clinical Pearls for NEET-PG: * **SNOUT:** **S**ensitivity rules **OUT** disease (when a highly sensitive test is negative). * **SPIN:** **S**pecificity rules **IN** disease (when a highly specific test is positive). * **Prevalence Impact:** Sensitivity and Specificity are **independent** of disease prevalence, whereas PPV and NPV are directly affected by it. * **Screening vs. Diagnosis:** Screening tests require high sensitivity; confirmatory tests require high specificity.

Q: What is the first quartile of the following data set: 1, 3, 4, 2?

1.5. ### Explanation **1. Understanding the Concept (Why 1.5 is correct)** In biostatistics, quartiles divide a sorted data set into four equal parts. To find the First Quartile ($Q_1$), follow these steps: * **Step 1: Arrange data in ascending order:** 1, 2, 3, 4. * **Step 2: Determine the position of $Q_1$:** For a small data set ($n=4$), the formula for the position is $\frac{n+1}{4}$. * Position = $\frac{4+1}{4} = 1.25$. * **Step 3: Calculate the value:** Since the position is 1.25, $Q_1$ lies between the 1st and 2nd values. * $Q_1 = \text{1st value} + 0.25 \times (\text{2nd value} - \text{1st value})$ * $Q_1 = 1 + 0.25 \times (2 - 1) = \mathbf{1.25}$ (In many simplified NEET-PG contexts, the average of the lower half is used). * **Alternative Method (Tukey’s):** Find the median of the lower half. The median of {1, 2, 3, 4} is 2.5. The lower half is {1, 2}. The mean of 1 and 2 is **1.5**. **2. Analysis of Incorrect Options** * **Option A (1):** This is the minimum value (0th percentile), not the first quartile. * **Option B (3):** This represents the Third Quartile ($Q_3$) or the 75th percentile for this data set. * **Option D (4):** This is the maximum value (100th percentile). **3. Clinical Pearls & High-Yield Facts** * **Interquartile Range (IQR):** $Q_3 - Q_1$. It represents the middle 50% of the data and is the preferred measure of dispersion for skewed data. * **Box-and-Whisker Plot:** The "box" represents the IQR, with the central line marking the Median ($Q_2$). * **Relationship:** $Q_1$ = 25th percentile; $Q_2$ = 50th percentile (Median); $Q_3$ = 75th percentile. * **NEET-PG Tip:** If the data set is small and even, always remember to sort the numbers first; skipping this step is the most common cause of error.

Q: A city has a population of 10000 with 500 diabetic patients. A new diagnostic test is applied to this population. The test gives a true positive result in 350 patients and a false positive result in 1900 patients. Which of the following statements regarding the test are true?

Prevalence is 5%, Sensitivity is 70%, Specificity is 80%. To solve this problem, we must first organize the data into a **2x2 contingency table**. **Given Data:** * Total Population ($N$) = 10,000 * Total Diseased ($D+$) = 500 (Diabetic patients) * Total Non-diseased ($D-$) = $10,000 - 500 = 9,500$ * True Positives ($TP$) = 350 * False Positives ($FP$) = 1,900 ### 1. Calculations * **Prevalence:** (Total cases / Total population) × 100 * $500 / 10,000 \times 100 = \mathbf{5\%}$ * **Sensitivity:** (True Positives / Total Diseased) × 100 * $350 / 500 \times 100 = \mathbf{70\%}$ * **Specificity:** (True Negatives / Total Non-diseased) × 100 * First, find True Negatives ($TN$): $9,500 (Total\ D-) - 1,900 (FP) = 7,600$ * $7,600 / 9,500 \times 100 = \mathbf{80\%}$ ### 2. Analysis of Options * **Option A is correct** because it accurately reflects all three calculated parameters. * **Option B** is incomplete as it omits the prevalence, which is a key epidemiological metric requested by the context of the question. * **Options C and D** are incorrect because they state a specificity of 70%. A specificity of 70% would imply 2,850 false positives ($9,500 \times 0.30$), which contradicts the provided data of 1,900. ### 3. NEET-PG High-Yield Pearls * **Sensitivity (SNoP):** Sensitivity rules **OUT** the disease when the test is negative. It is the ability of a test to identify true cases. * **Specificity (SPiN):** Specificity rules **IN** the disease when the test is positive. It is the ability to identify those without the disease. * **Prevalence:** Unlike sensitivity and specificity, **Predictive Values (PPV/NPV)** are heavily dependent on the prevalence of the disease in the population. As prevalence increases, PPV increases and NPV decreases.

Q: All of the following are inherent properties of a screening test EXCEPT?

Yield. **Explanation:** In epidemiology, it is crucial to distinguish between the **inherent properties** of a screening test and its **performance/outcome** in a specific population. **Why "Yield" is the correct answer:** **Yield** is not an inherent property of the test itself. Instead, it refers to the amount of previously unrecognized disease that is diagnosed as a result of the screening program. Yield depends on external factors such as the **prevalence** of the disease in the community, the participation rate (compliance), and the frequency of screening. A test with high sensitivity will still have a low yield if the disease is rare in the population being screened. **Analysis of Incorrect Options:** * **A & B. Sensitivity and Specificity:** These are the "stable" inherent properties of a test. They measure the test's ability to correctly identify diseased and non-diseased individuals, respectively. They are generally independent of disease prevalence and depend on the test's design and cut-off points. * **D. Predictive Accuracy:** This refers to the ability of a test to predict the presence or absence of disease (often linked to the overall validity). While Predictive Values (PPV/NPV) change with prevalence, the basic "accuracy" (the proportion of all tests that are correct) is considered a metric of the test's diagnostic performance. **High-Yield Pearls for NEET-PG:** * **Sensitivity:** Probability that a test is positive when the patient has the disease (True Positive Rate). * **Specificity:** Probability that a test is negative when the patient does not have the disease (True Negative Rate). * **Predictive Value:** Unlike sensitivity/specificity, **Positive Predictive Value (PPV)** is highly dependent on prevalence. If prevalence increases, PPV increases. * **Yield formula:** Yield = Sensitivity × Prevalence. This clearly shows that Yield is a product of the test property and the population characteristic.

Question 1

In a study to establish smoking as a risk factor for disease, 30 out of 50 smokers developed disease while 10 out of 50 non-smokers developed disease. Calculate the Odds Ratio.

Accepted Answer

5

Answer

3

Answer

6

Answer

10

Question 2

The Child Pugh score is a type of what kind of scale?

Accepted Answer

Ordinal

Answer

Nominal

Answer

Quantitative

Answer

Qualitative

Question 3

While testing a hypolipidemic drug, serum lipid levels were tested both before and after its use. Which statistical test is best suited for the analysis of the results?

Accepted Answer

Paired T test

Answer

Student's T test

Answer

Chi-square test

Answer

None of the above

Question 4

In a case-control study of a suspected association between breast cancer and the contraceptive pill, all of the following are true statements except?

Accepted Answer

The attributable risk of breast cancer resulting from the pill may be directly measured.

Answer

The control should come from a population that has the same potential for breast cancer as the cases.

Answer

The control should exclude women known to be taking the pill at the time of the survey.

Answer

All the controls need to be healthy.

Question 5

A diagnostic test was performed. Out of 80 individuals who tested positive, 40 did not have the disease. Out of 9920 individuals who tested negative, 9840 did not have the disease. What is the sensitivity of the test?

Accepted Answer

33%

Answer

13%

Answer

50%

Answer

99%

Question 6

Which epidemiological study provides the most accurate results?

Accepted Answer

Meta-analysis

Answer

Cross-sectional study

Answer

Randomized controlled trial with double blinding

Answer

Cohort study

Question 7

What is the first quartile of the following data set: 1, 3, 4, 2?

Accepted Answer

1.5

Answer

1

Answer

3

Answer

4

Question 8

A city has a population of 10000 with 500 diabetic patients. A new diagnostic test is applied to this population. The test gives a true positive result in 350 patients and a false positive result in 1900 patients. Which of the following statements regarding the test are true?

Accepted Answer

Prevalence is 5%, Sensitivity is 70%, Specificity is 80%

Answer

Sensitivity is 70%, Specificity is 80%

Answer

Prevalence is 5%, Sensitivity is 70%, Specificity is 70%

Answer

Sensitivity is 70%, Specificity is 70%

Question 9

Which of the following statements regarding Meta-analysis is FALSE?

Accepted Answer

The validity of a meta-analysis does not depend on the quality of the systematic review.

Answer

Its primary purpose is not to identify risk factors.

Answer

Its purpose is to increase statistical power by increasing the sample size.

Answer

It is a statistical technique for combining the findings from several independent studies on a specific topic.

Question 10

All of the following are inherent properties of a screening test EXCEPT?

Accepted Answer

Yield

Answer

Sensitivity

Answer

Specificity

Answer

Predictive accuracy

Biostatistics — MCQs

Biostatistics — MCQs

On this page

Practice by Chapter

Want unlimited practice?