Biostatistics Practice Questions

Q: All of the following are true about case-control studies except?

Less prone to bias. **Explanation:** The correct answer is **D (Less prone to bias)** because case-control studies are, in fact, **highly prone to bias**, particularly **Recall Bias** and **Selection Bias**. Since these studies are retrospective (looking backward from effect to cause), they rely heavily on the memory of participants or past medical records, which may be incomplete or inaccurate. **Analysis of Options:** * **A. Rapid and inexpensive:** This is true. Unlike cohort studies, case-control studies do not require long-term follow-up. They use existing data or interviews, making them the quickest and cheapest observational study design. * **B. No risk to subjects:** This is true. Because the investigator does not intervene or administer any drug/exposure (it is purely observational) and the outcome has already occurred, there is no physical risk to the participants. * **C. Risk factor can be identified:** This is true. Case-control studies are ideal for generating hypotheses and identifying potential risk factors (associations) for a disease, especially rare diseases. **Clinical Pearls for NEET-PG:** * **Measure of Association:** The Odds Ratio (OR) is the key statistic used in case-control studies. * **Rare Diseases:** Case-control studies are the **study of choice** for rare diseases (e.g., specific cancers). * **Nesting:** A "Nested Case-Control Study" is one conducted within a large cohort study, which helps reduce selection and information bias. * **Direction:** The direction of inquiry is **Backward** (Effect $\rightarrow$ Cause).

Q: The validity of a diagnostic test is based upon all of the following except:

Precision. ### Explanation The core concept of this question lies in distinguishing between **Validity** and **Reliability** in biostatistics. **Why Precision is the Correct Answer:** **Precision** (also known as **Reliability** or Repeatability) refers to the consistency of a test. It is the ability of a test to produce the same results when repeated under the same conditions. While a test can be highly precise (giving the same result every time), it may still be wrong. Therefore, precision is a measure of consistency, not validity. **Why the other options are incorrect (Components of Validity):** **Validity** (also known as **Accuracy**) refers to the ability of a test to measure what it is actually intended to measure—how close the result is to the "true" value (Gold Standard). * **Sensitivity (Option A):** A component of validity; it measures the ability of a test to correctly identify those with the disease (True Positives). * **Specificity (Option B):** A component of validity; it measures the ability of a test to correctly identify those without the disease (True Negatives). * **Accuracy (Option D):** This is the overall validity of the test, calculated as $(TP + TN) / \text{Total}$. --- ### High-Yield Clinical Pearls for NEET-PG: 1. **Validity vs. Reliability Analogy:** Think of a dartboard. * **Valid:** Hitting the bullseye. * **Reliable/Precise:** Hitting the same spot repeatedly (even if it's not the bullseye). 2. **Sensitivity** is used for **Screening** (to rule out disease - SNOUT). 3. **Specificity** is used for **Confirmation** (to rule in disease - SPIN). 4. **Predictive Values** (PPV/NPV) are not inherent properties of a test; they depend heavily on the **prevalence** of the disease in the population, whereas Sensitivity and Specificity remain constant.

Q: The probability of developing Acute Myocardial Infarction (AMI) in a lifetime is 0.75. What are the odds of developing AMI in a lifetime?

3:1. **Explanation:** In biostatistics, it is crucial to distinguish between **Probability** and **Odds**. * **Probability (P):** The likelihood of an event occurring out of the total number of possibilities. It is expressed as: $P = \frac{\text{Events}}{\text{Total Outcomes}}$. * **Odds:** The ratio of the probability of an event occurring to the probability of it *not* occurring. It is expressed as: $\text{Odds} = \frac{P}{1 - P}$. **Calculation for this question:** 1. Given Probability ($P$) = 0.75 (or 3/4). 2. Probability of the event NOT occurring ($1 - P$) = $1 - 0.75 = 0.25$ (or 1/4). 3. $\text{Odds} = \frac{0.75}{0.25} = \frac{3}{1}$ or **3:1**. **Analysis of Options:** * **Option A (3:1):** Correct. For every 4 people, 3 will develop AMI and 1 will not. * **Option B (3:4):** Incorrect. This represents the probability (0.75) expressed as a ratio, not the odds. * **Option C (4:3):** Incorrect. This is the reciprocal of the probability, sometimes used to calculate "Number Needed to Treat" (NNT) in different contexts, but mathematically irrelevant here. * **Option D (1:3):** Incorrect. These are the "odds against" the event, or the odds of *not* developing AMI. **High-Yield Clinical Pearls for NEET-PG:** * **Range:** Probability always ranges between **0 and 1**, whereas Odds can range from **0 to infinity**. * **Case-Control Studies:** The **Odds Ratio (OR)** is the standard measure of association because these studies do not allow for the calculation of incidence or Relative Risk (RR). * **Rare Disease Assumption:** If a disease is rare (prevalence <10%), the Odds Ratio becomes a good approximation of the Relative Risk.

Q: A study on a group of patients showed the coefficient of variance for BP and serum creatinine to be 20% and 15% respectively. What can be inferred?

The variation in BP is more than in serum creatinine.. ### Explanation **1. Why the Correct Answer is Right:** The **Coefficient of Variation (CV)** is a measure of **relative variation**. It is calculated as: $$CV = \frac{\text{Standard Deviation (SD)}}{\text{Mean}} \times 100$$ Unlike Standard Deviation, which measures absolute dispersion in the same units as the data, CV is a dimensionless percentage. It allows for the comparison of variability between two different datasets with different units (e.g., mmHg for BP vs. mg/dL for Creatinine). A **higher CV indicates greater relative dispersion** or less consistency. Since the CV for BP (20%) is higher than for serum creatinine (15%), the variation in BP is relatively greater. **2. Why the Incorrect Options are Wrong:** * **Option B:** This is mathematically incorrect because 15% (Creatinine) is less than 20% (BP). * **Options C & D:** These are incorrect because CV depends on both the SD **and** the Mean. We cannot determine the absolute Standard Deviation without knowing the mean values of the two groups. For example, a high SD with an even higher Mean could result in a low CV. Therefore, CV only tells us about *relative* variation, not *absolute* SD. **3. High-Yield Clinical Pearls for NEET-PG:** * **Unitless Measure:** CV is the best measure to compare the precision of two different laboratory instruments or datasets with different units. * **Precision vs. Accuracy:** In lab medicine, a lower CV indicates higher **precision** (reproducibility). * **Standard Deviation (SD):** Measures the dispersion of data around the mean in a distribution. 1 SD covers 68.2% of values, 2 SD covers 95.4%, and 3 SD covers 99.7% in a Normal Distribution. * **Standard Error (SE):** Measures the dispersion of "sample means" around the "population mean." It is used to calculate Confidence Intervals.

Q: More false positive cases in a community signify that the disease has:

Low prevalence. This question tests the relationship between **Prevalence** and the **Positive Predictive Value (PPV)** of a screening test. ### Why "Low Prevalence" is Correct The number of false positives is inversely related to the prevalence of a disease in a population. * **Positive Predictive Value (PPV)** is the probability that a person who tests positive actually has the disease. * When a disease is rare (**Low Prevalence**), the vast majority of the population is healthy (True Negatives). Even a highly specific test will produce some false positives. Because the actual number of diseased individuals is so small, these "false positives" from the healthy group will outnumber the "true positives" from the diseased group. * Therefore, in a low-prevalence setting, a positive test result is more likely to be a **False Positive** than a True Positive. ### Why Other Options are Incorrect * **A. High Prevalence:** In a high-prevalence population, the number of True Positives increases significantly, which increases the PPV and decreases the proportion of false positives among those who test positive. * **B. High Sensitivity:** Sensitivity relates to the test's ability to identify true cases. High sensitivity reduces **False Negatives**, not false positives. * **D. Low Sensitivity:** Low sensitivity means the test misses many actual cases, leading to more **False Negatives**. ### NEET-PG High-Yield Pearls 1. **Prevalence vs. Predictive Value:** * Prevalence $\uparrow$ = PPV $\uparrow$ (and False Positives $\downarrow$) * Prevalence $\downarrow$ = NPV $\uparrow$ (and False Negatives $\downarrow$) 2. **Screening Strategy:** To minimize false positives in a community, we use a test with **High Specificity**. 3. **Bayes' Theorem:** This is the mathematical principle underlying why predictive values change with prevalence, while Sensitivity and Specificity remain constant properties of the test itself.

Q: What is the term for the number of abortions performed divided by the number of live births in the same period?

Abortion ratio. ### Explanation **1. Why "Abortion Ratio" is Correct:** In biostatistics, a **ratio** expresses a relationship between two independent quantities where the numerator is **not** a part of the denominator ($x/y$). * **Abortion Ratio** = $\frac{\text{Total number of abortions}}{\text{Total number of live births}} \times 1000$ * This indicator measures the "relative reproductive loss" compared to successful deliveries. Since an abortion is not a live birth, the numerator and denominator are mutually exclusive, making it a classic ratio. **2. Why Other Options are Incorrect:** * **Abortion Rate (Option A):** A rate typically implies that the numerator is part of the denominator and is calculated against the population at risk. The **Abortion Rate** is defined as the number of abortions per 1,000 women of reproductive age (15–44 years). * **Abortion Incidence (Option B):** Incidence refers to the number of *new* cases in a population at risk over a specific period. While abortions are incident events, the specific formula provided in the question (using live births as the denominator) is the formal definition of the "Ratio." * **Abortion Prevalence (Option C):** Prevalence refers to the total number of cases (old + new) existing in a population at a given time. It is not used to describe abortion data, as abortion is a discrete event, not a chronic state. **3. High-Yield Clinical Pearls for NEET-PG:** * **Abortion Ratio:** Numerator = Abortions; Denominator = **Live Births**. * **Abortion Rate:** Numerator = Abortions; Denominator = **Women (15-44 years)**. * **Key Distinction:** The Abortion Ratio is the best indicator of the "burden" of abortion relative to live births, whereas the Abortion Rate reflects the "risk" of abortion among women of childbearing age. * **Maternal Mortality Ratio (MMR):** Similarly uses **Live Births** as the denominator, making it a ratio, not a true rate.

Q: For a Chi-square test involving a contingency table with 4 rows and 5 columns, what is the degree of freedom?

12. ### Explanation **1. Why Option C (12) is Correct:** In biostatistics, the **Chi-square ($\chi^2$) test** is used to determine if there is a significant association between two categorical variables. The **Degrees of Freedom (df)** represent the number of values in the final calculation that are free to vary. For a contingency table (cross-tabulation), the formula for degrees of freedom is: **$df = (r - 1) \times (c - 1)$** *Where $r$ = number of rows and $c$ = number of columns.* Applying the formula to this question: * Rows ($r$) = 4 * Columns ($c$) = 5 * $df = (4 - 1) \times (5 - 1)$ * $df = 3 \times 4 = \mathbf{12}$ **2. Why Other Options are Incorrect:** * **Option A (20):** This is simply the product of rows and columns ($4 \times 5$). It fails to account for the fixed marginal totals in a contingency table. * **Option B (16):** This might result from an incorrect calculation like $(r \times c) - r$ or $(r \times c) - c$. * **Option D (9):** This would be the result if the table were $4 \times 4$, i.e., $(4-1) \times (4-1) = 9$. **3. Clinical Pearls & High-Yield Facts for NEET-PG:** * **Type of Data:** Chi-square is used for **qualitative (categorical)** data, not quantitative data. * **Null Hypothesis ($H_0$):** It tests the null hypothesis that there is no association between the two variables. * **Yates’ Correction:** Used specifically for a **$2 \times 2$ table** when the expected frequency in any cell is less than 5. * **Non-Parametric Test:** Chi-square is a non-parametric test (it does not assume a normal distribution). * **Proportions:** It is the test of choice for comparing more than two proportions.

Q: Which of the following statements is NOT true about correlation?

It indicates the risk of a disease.. ### Explanation **1. Why Option B is the Correct Answer (The "NOT True" statement):** Correlation (represented by the coefficient **'r'**) measures the strength and direction of a linear relationship between two continuous variables. It does **not** measure the risk of a disease. In epidemiology, "risk" is quantified using measures like **Relative Risk (RR)** or **Odds Ratio (OR)**, which are derived from 2x2 contingency tables in cohort or case-control studies. Correlation merely suggests that as one variable changes, the other tends to change, but it cannot quantify the probability of an outcome occurring. **2. Analysis of Other Options:** * **Option A (It does not indicate causation):** This is a fundamental rule of statistics (*Correlation is not causation*). Even a perfect correlation of +1.0 does not prove that one variable causes the other; they may both be influenced by a third "confounding" factor. * **Option C (A correlation of -1.0 shows a linear relationship):** This is true. The correlation coefficient ranges from **-1 to +1**. A value of -1 indicates a **perfect negative linear relationship** (as one variable increases, the other decreases in a straight line). * **Option D (It indicates an association):** This is true. Correlation is a statistical tool used to identify if an association exists between two quantitative variables (e.g., height and weight). **3. NEET-PG High-Yield Pearls:** * **Range of 'r':** Always between -1 and +1. * **Coefficient of Determination ($r^2$):** Represents the proportion of variance in one variable explained by the other. (e.g., if $r = 0.6$, then $r^2 = 0.36$ or 36%). * **Scatter Diagram:** The best visual method to represent correlation. * **Regression vs. Correlation:** Correlation measures the *strength* of association; Regression allows for the *prediction* of one variable based on another.

Q: In a study investigating smoking as a cause of lung cancer, 90 cases were identified. Among these cases, 75 were smokers. Additionally, 100 controls were selected, and 40 of them were smokers. Calculate the odds ratio for smoking as a risk factor for lung cancer?

7.5. ### Explanation **1. Understanding the Correct Answer (C: 7.5)** The study described is a **Case-Control Study**, as it starts with known outcomes (cases and controls) and looks backward to determine exposure (smoking). The appropriate measure of association for a case-control study is the **Odds Ratio (OR)**. To calculate the OR, we first arrange the data into a 2x2 contingency table: | | Cases (Lung Cancer) | Controls (No Cancer) | | :--- | :---: | :---: | | **Exposed (Smokers)** | 75 (a) | 40 (b) | | **Non-exposed (Non-smokers)** | 15 (c) | 60 (d) | | **Total** | **90** | **100** | *Note: Non-smokers are calculated by subtracting smokers from the total (90-75=15; 100-40=60).* **Formula for Odds Ratio:** $OR = \frac{a \times d}{b \times c}$ (Cross-product ratio) $OR = \frac{75 \times 60}{40 \times 15} = \frac{4500}{600} = \mathbf{7.5}$ An OR of 7.5 indicates that the odds of exposure (smoking) are 7.5 times higher in cases than in controls. **2. Why Other Options are Incorrect** * **A (2.0) & B (4.5):** These values result from calculation errors, such as using the wrong denominators or failing to calculate the non-exposed group correctly. * **D (10.0):** This might result from incorrectly dividing the total number of cases by the non-exposed controls. **3. NEET-PG High-Yield Pearls** * **Odds Ratio (OR):** Also known as the "Cross-product ratio." It is the only measure of association available in Case-Control studies because incidence cannot be calculated. * **Relative Risk (RR):** Used in **Cohort studies**. It requires the "Incidence" of the disease. * **OR ≈ RR:** The Odds Ratio is a good approximation of Relative Risk only when the disease is **rare** (the "Rare Disease Assumption"). * **Interpretation:** If OR > 1, the factor is a risk factor; if OR = 1, there is no association; if OR < 1, the factor is protective.

Question 1

All of the following are true about case-control studies except?

Accepted Answer

Less prone to bias

Answer

Rapid and inexpensive

Answer

No risk to subjects

Answer

Risk factor can be identified

Question 2

The validity of a diagnostic test is based upon all of the following except:

Accepted Answer

Precision

Answer

Sensitivity

Answer

Specificity

Answer

Accuracy

Question 3

The probability of developing Acute Myocardial Infarction (AMI) in a lifetime is 0.75. What are the odds of developing AMI in a lifetime?

Accepted Answer

3:1

Answer

3:4

Answer

4:3

Answer

1:3

Question 4

A study on a group of patients showed the coefficient of variance for BP and serum creatinine to be 20% and 15% respectively. What can be inferred?

Accepted Answer

The variation in BP is more than in serum creatinine.

Answer

The variation in serum creatinine is more than in BP.

Answer

The standard deviation of BP is more than of creatinine.

Answer

The standard deviation of creatinine is more than of BP.

Question 5

More false positive cases in a community signify that the disease has:

Accepted Answer

Low prevalence

Answer

High prevalence

Answer

High sensitivity

Answer

Low sensitivity

Question 6

What is the term for the number of abortions performed divided by the number of live births in the same period?

Accepted Answer

Abortion ratio

Answer

Abortion rate

Answer

Abortion incidence

Answer

Abortion prevalence

Question 7

For a Chi-square test involving a contingency table with 4 rows and 5 columns, what is the degree of freedom?

Accepted Answer

12

Answer

20

Answer

16

Answer

9

Question 8

Which of the following statements is NOT true about correlation?

Accepted Answer

It indicates the risk of a disease.

Answer

It does not indicate causation.

Answer

A correlation of -1.0 shows a linear relationship.

Answer

It indicates an association between two variables.

Question 9

Chi-square test is used for which of the following comparisons?

Accepted Answer

Comparing percentages, proportions, and fractions in two or more different groups of individuals

Answer

Comparing percentages, proportions, and fractions in paired data

Answer

Comparing percentages, proportions, and fractions in matched paired data

Answer

Comparing percentages, proportions, and fractions in two unpaired samples

Question 10

In a study investigating smoking as a cause of lung cancer, 90 cases were identified. Among these cases, 75 were smokers. Additionally, 100 controls were selected, and 40 of them were smokers. Calculate the odds ratio for smoking as a risk factor for lung cancer?

Accepted Answer

7.5

Answer

2.0

Answer

4.5

Answer

10.0

Biostatistics — MCQs

Biostatistics — MCQs

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