Biostatistics Practice Questions

Q: Which of the following statements regarding a case-control study is true?

It is useful for studying rare diseases.. **Explanation:** In a **Case-Control Study**, the investigator starts with the "effect" (disease) and looks backward to identify the "cause" (exposure). This retrospective nature makes it the design of choice for **rare diseases**. Because researchers can specifically select individuals who already have the disease (cases), they do not have to wait for the disease to develop in a large population over time, which would be impractical for rare conditions. **Analysis of Options:** * **Option A (Correct):** Since cases are pre-selected, it is highly efficient for studying diseases with low prevalence or long latency periods. * **Option B (Incorrect):** Incidence (the number of new cases in a population at risk) cannot be calculated because the researcher determines the number of cases at the start. Incidence can only be calculated in **Cohort studies**. * **Option C (Incorrect):** Case-control studies are generally **quick and inexpensive** because the outcome has already occurred, requiring no long-term follow-up. * **Option D (Incorrect):** Relative Risk (RR) requires incidence data. Therefore, in case-control studies, we use **Odds Ratio (OR)** as an estimate of the risk. **High-Yield Pearls for NEET-PG:** * **Direction:** Backward (Effect to Cause). * **Measure of Association:** Odds Ratio (OR). * **Key Bias:** Recall Bias (patients with the disease are more likely to remember past exposures). * **Matching:** Used in case-control studies to eliminate the effects of confounding variables. * **Sequence:** It is often the first step in testing a hypothesis before committing to a prospective cohort study.

Q: A village is divided into 5 lanes, and then each lane is sampled randomly. This is an example of which type of sampling?

Stratified random sampling. **Explanation:** The correct answer is **Stratified Random Sampling**. In this scenario, the village population is divided into non-overlapping subgroups (lanes) called **strata**. The key characteristic of stratified sampling is that the population is divided into groups based on a specific characteristic (in this case, geographical location/lanes), and then a **random sample is drawn from each and every stratum**. This ensures that every lane is represented in the final sample, reducing sampling error and ensuring better representativeness of the entire village. **Why other options are incorrect:** * **Simple Random Sampling:** Here, every individual in the entire village would have an equal chance of being selected directly (e.g., using a random number table for the whole population) without first dividing them into lanes. * **Systematic Random Sampling:** This involves selecting individuals at fixed intervals (the $k^{th}$ unit) from a list, such as picking every $5^{th}$ house in the village after a random start. * **Cluster Sampling (Distinction):** Often confused with stratified sampling, in cluster sampling, the village would be divided into lanes, but only a *few* lanes would be randomly selected, and everyone within those selected lanes would be studied. In this question, *each* lane is sampled, which defines stratification. **High-Yield Pearls for NEET-PG:** * **Stratified Sampling:** Best when the population is **heterogeneous**; it ensures sub-group representation. * **Cluster Sampling:** Best when the population is large and widely dispersed; the unit of randomization is a "cluster" (e.g., a village or a school) rather than an individual. * **Multistage Sampling:** Used in large-scale national surveys (like NFHS), involving multiple levels of sampling (e.g., State → District → Village → Household).

Q: In a study comparing women with breast cancer to those without, 75 out of 100 cases used calcium supplements, while 25 out of 100 non-cases used calcium supplements. Calculate the cross-product ratio.

9. ### Explanation **1. Why Option A is Correct** The **Cross-Product Ratio** is another name for the **Odds Ratio (OR)**, which is the standard measure of association in a Case-Control study. It represents the ratio of the odds of exposure among cases to the odds of exposure among controls. To calculate this, we first arrange the data into a **2x2 Contingency Table**: | | Cases (Cancer) | Controls (No Cancer) | | :--- | :---: | :---: | | **Exposed (Calcium)** | 75 (a) | 25 (b) | | **Non-Exposed** | 25 (c) | 75 (d) | *Note: If 75/100 cases used supplements, 25 did not. If 25/100 controls used supplements, 75 did not.* **Formula:** $$\text{Odds Ratio} = \frac{a \times d}{b \times c}$$ $$\text{Calculation} = \frac{75 \times 75}{25 \times 25} = \frac{5625}{625} = \mathbf{9}$$ An OR of 9 indicates that the odds of exposure to calcium supplements were 9 times higher in women with breast cancer compared to those without. **2. Why Other Options are Incorrect** * **Options B (6), C (3), and D (12):** These are mathematical errors. They result from incorrectly setting up the 2x2 table (e.g., using the total 100 as a denominator instead of the non-exposed count) or failing to use the cross-multiplication method. **3. NEET-PG High-Yield Clinical Pearls** * **Study Design:** The Odds Ratio is used for **Case-Control studies**, while Relative Risk (RR) is used for **Cohort studies**. * **Interpretation:** * OR > 1: Positive association (Risk factor). * OR = 1: No association. * OR < 1: Negative association (Protective factor). * **Key Tip:** In the exam, always ensure you calculate the "non-exposed" cells ($c$ and $d$) by subtracting the exposed from the total before applying the formula. Do not use the total (100) in the cross-product.

Q: Which of the following statements regarding case-control studies is false?

They are used to calculate the relative risk.. **Explanation:** In epidemiology, the choice of study design dictates which measures of association can be calculated. **Why Option A is the Correct (False) Statement:** Relative Risk (RR) measures the **incidence** of a disease in an exposed group versus an unexposed group. Because a Case-Control study starts with people who already have the disease (cases) and looks backward in time (retrospective), it cannot determine the incidence (new cases over time). Therefore, **Relative Risk cannot be directly calculated** in case-control studies. RR is the hallmark of Cohort studies. **Analysis of Other Options:** * **Option B (Odds Ratio):** Since we cannot calculate incidence, we use the **Odds Ratio (OR)** as a proxy for relative risk in case-control studies. It compares the odds of exposure among cases to the odds of exposure among controls. * **Option C (Inexpensive):** Case-control studies are generally quicker and cheaper than cohort studies because they do not require long-term follow-up or large sample sizes. * **Option D (Rare Diseases):** This is the study design of choice for rare diseases. Since you start by identifying "cases," you don't have to wait years for a rare condition to develop in a large population. **NEET-PG High-Yield Pearls:** * **Directionality:** Case-control studies proceed from **Effect to Cause** (Retrospective). * **Matching:** This technique is used in case-control studies to eliminate **confounding factors**. * **Recall Bias:** This is the most common bias in case-control studies, as cases may remember past exposures more vividly than healthy controls. * **Neyman Bias:** Also known as prevalence-incidence bias; it occurs when cases are selected from survivors (prevalent cases) rather than new (incident) cases.

Q: What is true about the reliability of a test?

It gives the same results on repeated tests.. **Explanation:** **Reliability** (also known as precision, consistency, or reproducibility) refers to the ability of a test to yield the same results when repeated under similar conditions. In biostatistics, it measures how free a test is from random error. If a test is reliable, repeated measurements on the same stable subject will produce consistent values. **Analysis of Options:** * **Option C (Correct):** This is the literal definition of reliability. It ensures that the results are stable over time and across different observers (inter-rater reliability). * **Option A:** Incorrect. Consistency and reproducibility are the *core* components of reliability; they are the primary focus, not a "non-problem." * **Option B:** Incorrect. While an investigator's skill can reduce observer bias, reliability is a property of the test/instrument itself. High reliability should ideally minimize the impact of an individual investigator's subjective knowledge. * **Option D:** Incorrect. This describes "Validity." Validity (accuracy) is the extent to which a test measures what it is actually intended to measure. **High-Yield NEET-PG Pearls:** 1. **Reliability vs. Validity:** A test can be reliable but not valid (e.g., a weighing scale that consistently shows 5kg extra is reliable but inaccurate). However, for a test to be highly valid, it must be reliable. 2. **Evaluation:** Reliability is measured using the **Kappa Coefficient** (for qualitative data) or **Intraclass Correlation Coefficient** (for quantitative data). 3. **Factors affecting Reliability:** * **Observer Variation:** Intra-observer (same person) vs. Inter-observer (different people). * **Biological Variation:** Changes in the parameter being measured (e.g., BP fluctuations). * **Instrument Error:** Faulty equipment.

Q: A drug company is developing a new pregnancy-test kit for use on an outpatient basis. The company used the pregnancy test on 100 women who are known to be pregnant. Out of 100 women, 99 showed positive test. Upon using the same test on 100 non-pregnant women, 90 showed negative result. What is the sensitivity of the test?

99%. ### Explanation **1. Why the Correct Answer (B) is Right:** Sensitivity is defined as the ability of a test to correctly identify those **with the disease** (True Positives). It is calculated using the formula: $$\text{Sensitivity} = \frac{\text{True Positives (TP)}}{\text{Total Diseased (TP + FN)}} \times 100$$ In this scenario: * **Total pregnant women (Diseased):** 100 * **Positive results (True Positives):** 99 * **Sensitivity:** $(99 / 100) \times 100 = 99\%$ The test correctly identified 99% of the women who were actually pregnant. **2. Why the Incorrect Options are Wrong:** * **Option A (90%):** This represents the **Specificity** of the test. Specificity is the ability to correctly identify those **without the disease** (True Negatives). Here, 90 out of 100 non-pregnant women tested negative ($90/100 = 90\%$). * **Option C (Average):** Sensitivity and Specificity are independent properties of a diagnostic test. Averaging them has no statistical significance in evaluating test performance. * **Option D:** The data provided is sufficient. We have the "Gold Standard" status (known pregnant vs. non-pregnant) and the test results, which are the only requirements to build a 2x2 contingency table. **3. Clinical Pearls & High-Yield Facts for NEET-PG:** * **SNOUT:** **S**ensitivity rules **OUT** a disease when the result is negative (useful for screening). * **SPIN:** **S**pecificity rules **IN** a disease when the result is positive (useful for confirmation). * **Sensitivity** is also known as the **True Positive Rate**. * **False Negative Rate** = $100 - \text{Sensitivity}$. In this case, it is 1%. * **False Positive Rate** = $100 - \text{Specificity}$. In this case, it is 10%.

Q: Which of the following statements is true regarding epidemiological study designs?

Cohort studies involve a large number of subjects.. ### Explanation **Why Option B is Correct:** Cohort studies are **prospective** in nature, starting with a group of exposed and non-exposed individuals and following them over time to observe the development of an outcome (disease). Because the incidence of most diseases is relatively low, a **large sample size** is mandatory to ensure enough cases occur to achieve statistical significance. This makes them resource-intensive but ideal for calculating **Incidence** and **Relative Risk**. **Analysis of Incorrect Options:** * **Option A:** Case-control studies are actually the **study of choice for rare diseases**. Since they start with "cases" (people who already have the disease), researchers do not have to wait for rare events to occur naturally. * **Option C:** Case-control studies are **inexpensive** and easy to conduct because they use existing records or interviews and require smaller sample sizes compared to cohort studies. * **Option D:** Cohort studies are **time-consuming** because they require a long follow-up period (attrition/loss to follow-up is a major challenge). Case-control studies yield quicker results as the outcome has already occurred. **NEET-PG High-Yield Pearls:** * **Directionality:** Cohort is "Cause to Effect" (Prospective); Case-control is "Effect to Cause" (Retrospective). * **Measures of Association:** * Cohort Study $\rightarrow$ **Relative Risk (RR)** and Attributable Risk. * Case-control Study $\rightarrow$ **Odds Ratio (OR)**. * **Bias:** Case-control studies are highly prone to **Recall Bias**, while Cohort studies are prone to **Selection/Attrition Bias**. * **Nested Case-Control Study:** A hybrid design where a case-control study is conducted within an ongoing cohort study; it is more cost-effective than a full cohort.

Q: Selection bias occurs mainly in which type of study?

Case-control study. **Explanation:** **Selection bias** occurs when the study population does not accurately represent the target population due to the way participants are selected. **Why Case-Control Study is the correct answer:** In a case-control study, the researcher selects participants based on the **outcome** (presence or absence of disease) and then looks backward for exposure. This design is highly susceptible to selection bias because both the "cases" and "controls" are often chosen from hospital settings (Berkson’s bias) or through non-random methods. If the criteria for selecting cases differ systematically from those for selecting controls (other than the disease itself), the relationship between exposure and outcome becomes distorted. **Analysis of Incorrect Options:** * **Cohort Study:** While selection bias can occur (e.g., Healthy Worker Effect), these studies are more prone to **Follow-up bias (Attrition bias)** because they track participants over time. * **Randomized Controlled Trial (RCT):** The hallmark of an RCT is **Randomization**, which is specifically designed to eliminate selection bias by ensuring that every participant has an equal chance of being assigned to any group, thereby balancing both known and unknown confounders. * **All of the above:** While bias can exist in any study, it is "mainly" or most characteristically associated with Case-Control designs in epidemiological hierarchy. **High-Yield Clinical Pearls for NEET-PG:** * **Berkson’s Bias (Admission Rate Bias):** A specific type of selection bias occurring in hospital-based case-control studies. * **Neyman Bias (Prevalence-Incidence Bias):** Occurs when cases are selected from survivors (common in cross-sectional/case-control studies of fatal diseases). * **Recall Bias:** The most common type of **Information Bias** in case-control studies (not to be confused with selection bias). * **Gold Standard to eliminate Selection Bias:** Randomization.

Q: Which of the following statements about meta-analysis is FALSE?

It is easy to conduct.. ### Explanation **1. Why Option A is the Correct (False) Statement:** Meta-analysis is considered the highest level of evidence in the hierarchy of evidence-based medicine (EBM). Contrary to being "easy," it is a **complex, rigorous, and time-consuming** statistical process. It requires a systematic review of literature, stringent inclusion/exclusion criteria, assessment of study quality (risk of bias), and sophisticated statistical software to pool data. Therefore, the statement that it is "easy to conduct" is incorrect. **2. Analysis of Other Options:** * **Option B (Analysis of multiple analyses):** This is the literal definition of meta-analysis. It uses statistical methods to combine data from multiple independent studies (usually RCTs) to reach a single quantitative conclusion with higher statistical power. * **Option C (Funnel and Forest plots):** These are the hallmark graphical tools of meta-analysis. * **Forest Plot:** Displays the results of individual studies and the pooled estimate (represented by a diamond). * **Funnel Plot:** Used primarily to detect **publication bias**. * **Option D (Apples-and-oranges effect):** This refers to a common criticism/limitation of meta-analysis. It occurs when a researcher pools studies that are too clinically or methodologically diverse (heterogeneous) to be meaningfully combined, leading to invalid conclusions. **3. High-Yield Clinical Pearls for NEET-PG:** * **Hierarchy of Evidence:** Meta-analysis of RCTs > Systematic Review > RCT > Cohort > Case-Control > Case Series > Case Report. * **Heterogeneity:** Measured using the **I² statistic**. High I² (>50-75%) suggests studies are too different to pool. * **Publication Bias:** If the funnel plot is asymmetrical, it suggests publication bias (small negative studies are often not published). * **Fixed vs. Random Effects Model:** Used to pool data depending on the presence of heterogeneity.

Question 1

Which of the following statements regarding a case-control study is true?

Accepted Answer

It is useful for studying rare diseases.

Answer

Incidence can be calculated.

Answer

It takes a longer time to complete.

Answer

Relative risk can be calculated.

Question 2

What is the most common reason for a screening test to yield a high number of false positives?

Accepted Answer

Low prevalence of the disease in the population

Answer

High specificity

Answer

High sensitivity

Answer

High prevalence of the disease in the population

Question 3

A village is divided into 5 lanes, and then each lane is sampled randomly. This is an example of which type of sampling?

Accepted Answer

Stratified random sampling

Answer

Simple random sampling

Answer

Systematic random sampling

Answer

All of the above

Question 4

In a study comparing women with breast cancer to those without, 75 out of 100 cases used calcium supplements, while 25 out of 100 non-cases used calcium supplements. Calculate the cross-product ratio.

Accepted Answer

9

Answer

6

Answer

3

Answer

12

Question 5

Which of the following statements regarding case-control studies is false?

Accepted Answer

They are used to calculate the relative risk.

Answer

The odds ratio can be calculated.

Answer

They are relatively inexpensive.

Answer

They are useful for studying rare diseases.

Question 6

What is true about the reliability of a test?

Accepted Answer

It gives the same results on repeated tests.

Answer

Consistency and reproducibility of the test are not a problem.

Answer

Investigator's knowledge is important.

Answer

It is the extent of variation of measurement of contained behaviour.

Question 7

A drug company is developing a new pregnancy-test kit for use on an outpatient basis. The company used the pregnancy test on 100 women who are known to be pregnant. Out of 100 women, 99 showed positive test. Upon using the same test on 100 non-pregnant women, 90 showed negative result. What is the sensitivity of the test?

Accepted Answer

99%

Answer

90%

Answer

Average of 90% and 99%

Answer

Cannot be calculated from the given data

Question 8

Which of the following statements is true regarding epidemiological study designs?

Accepted Answer

Cohort studies involve a large number of subjects.

Answer

Case-control studies are not suitable for rare diseases.

Answer

Case-control studies are relatively expensive.

Answer

Cohort studies yield relatively quick results.

Question 9

Selection bias occurs mainly in which type of study?

Accepted Answer

Case-control study

Answer

Cohort study

Answer

Randomized controlled trial

Answer

All the above

Question 10

Which of the following statements about meta-analysis is FALSE?

Accepted Answer

It is easy to conduct.

Answer

It is an analysis of multiple analyses.

Answer

Funnel plots and forest plots are used in its interpretation.

Answer

An 'apple-oranges' effect can be observed in it.

Biostatistics — MCQs

Biostatistics — MCQs

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