Biostatistics Practice Questions

Q: Which one of the following epidemiologic methods can be used to identify risk factors and estimate the degree of risk?

Case control and Cohort studies. **Case control and Cohort studies** - **Case-control studies** effectively identify **risk factors** by comparing exposure histories between individuals with a disease (cases) and those without (controls). - **Cohort studies** directly estimate the **degree of risk** (e.g., relative risk, incidence rates) by following exposed and unexposed groups over time to observe disease development. *Cohort study and Randomized control trial* - While **cohort studies** identify risk factors, **randomized controlled trials (RCTs)** primarily evaluate the **efficacy of interventions** by randomly assigning exposure. - RCTs are ethically difficult to use for identifying harmful risk factors directly, as intentionally exposing participants to potential harm is generally not permissible. *Cohort study and Ecological studies* - **Ecological studies** examine disease rates and exposures at a **population level**, making them useful for generating hypotheses but not for establishing individual-level risk factors or degrees of risk due to the **ecological fallacy**. - They cannot directly link individual exposure to individual outcome. *Case control and Cross-sectional studies* - **Cross-sectional studies** assess prevalence and provide a snapshot of health status and exposure at a single point in time, but they cannot establish **temporality** or the degree of association between exposure and outcome. - They are useful for describing prevalence but not for inferring causality or precise risk.

Q: What is the relative risk of developing pulmonary embolism in users of oral contraceptives as per the information given below?

4.80. ***4.80*** - **Relative Risk (RR)** = Risk in exposed / Risk in unexposed - From the table provided: - **OC users (exposed):** 120 developed PE out of 200 women → Risk = 120/200 = 0.60 - **Non-OC users (unexposed):** 10 developed PE out of 80 women → Risk = 10/80 = 0.125 - **RR = 0.60 / 0.125 = 4.8** - This indicates OC users have **4.8 times higher risk** of developing pulmonary embolism compared to non-users - This significant association aligns with known **thrombogenic effects** of estrogen-containing oral contraceptives - **Clinical relevance:** Highlights importance of screening for VTE risk factors before prescribing OCs *0.24* - This value would result from incorrect calculation or misinterpretation of table values - Does not represent any valid epidemiological measure from the given data *0.48* - This is simply the decimal misplacement of 4.8 divided by 10 - Results from calculation error, not proper relative risk computation *2.40* - This is exactly half of the correct answer (4.8/2) - May result from using wrong numerator or denominator values - Does not represent the correct relative risk calculation

Q: In a case-control study, 300 women aged 20-45 years suffering from breast cancer were compared with age-matched 300 women without breast cancer. It was observed that 120 women among cases and 60 women among controls were obese. The odds ratio of developing breast cancer among obese women is:

8/3. **8/3** - The **odds ratio** is calculated using the formula (a×d) / (b×c) from a 2×2 contingency table. - Setting up the table: Cases (breast cancer) vs Controls (no breast cancer) by exposure (obesity status) - a = obese cases = 120 - b = obese controls = 60 - c = non-obese cases = 300 - 120 = 180 - d = non-obese controls = 300 - 60 = 240 - **Odds ratio = (120 × 240) / (60 × 180) = 28,800 / 10,800 = 8/3** - This indicates that **obese women have 2.67 times the odds** of developing breast cancer compared to non-obese women. *11/5* - This answer suggests an **incorrect calculation** of the odds ratio, likely due to misassignment of values in the 2×2 table or an arithmetic error. - The result (2.2) does not match the correct cross-product ratio from the given data. *11/3* - This value is not derived from the correct **odds ratio** calculation using the given data. - Errors in setting up the 2×2 table or confusing which cells represent **exposed cases**, **exposed controls**, **unexposed cases**, and **unexposed controls** lead to such incorrect results. *9/5* - This option would result from an **incorrect placement** of values when calculating the cross-product ratio. - Possibly from reversing the numerator and denominator components or miscalculating the unexposed groups.

Q: Predictive accuracy of a screening test depends on the following EXCEPT:

Disease incidence. ***Disease incidence*** - **Disease incidence** refers to the rate at which new cases of a disease occur in a population over a specified period. While related to disease prevalence, it is not a direct factor in calculating the predictive accuracy of a screening test. - Predictive accuracy, specifically **positive predictive value (PPV)** and **negative predictive value (NPV)**, relies on the test's inherent properties (sensitivity and specificity) and the **prevalence** of the disease, not its incidence. *Specificity of screening test* - **Specificity** is crucial for predictive accuracy as it determines the probability that a test correctly identifies those *without* the disease. - A test with high specificity will have fewer **false positives**, which directly impacts the positive predictive value. *Disease prevalence* - **Disease prevalence** profoundly influences the predictive accuracy of a screening test. The **positive predictive value** increases with higher disease prevalence. - In populations with low disease prevalence, even highly sensitive and specific tests can yield a large number of **false positives**. *Sensitivity of screening test* - **Sensitivity** is a key determinant of predictive accuracy, as it measures the proportion of *true positives* correctly identified by the test. - A test with high sensitivity helps ensure that most individuals *with* the disease are detected, which affects both **positive and negative predictive values**.

Q: What is the specificity of sputum microscopy in detection of Pulmonary Tuberculosis (PTB) as per the information given below?

90 %. ***90 %*** - Specificity is calculated as the number of **true negatives** divided by the sum of true negatives and **false positives**. - From the table: True Negatives = 180 (PTB Absent, Sputum Negative) and False Positives = 20 (PTB Absent, Sputum Positive). - Specificity = (180 / (180 + 20)) × 100 = (180 / 200) × 100 = **90%**. - This represents the ability of the test to correctly identify those **without the disease**. *36 %* - This value does not correspond to any standard diagnostic test metric such as sensitivity, specificity, positive predictive value, or negative predictive value based on the provided data. - It might be a miscalculation or a different ratio not typically used in this context. *94 %* - This value does not match any standard calculation from the given 2×2 table. - It may represent a misinterpretation of the data or an incorrect calculation. *10 %* - This value represents the **false positive rate** (1 - specificity). - Calculated as: False Positives / Total without disease = 20 / 200 = 10%. - It is the complement of specificity, not specificity itself.

Q: A research study comparing two groups shows a statistically significant difference (p=0.04) but the confidence interval is very wide (0.5 to 4.2). How should this result be interpreted?

The result is statistically significant but imprecise. ***The result is statistically significant but imprecise*** - A **p-value of 0.04** indicates **statistical significance** at the conventional ɑ=0.05 level, meaning the observed difference is unlikely due to chance. - A **wide confidence interval (0.5 to 4.2)** suggests that while the true effect is likely positive, its magnitude is **highly uncertain** or **imprecise**, possibly due to a small sample size. *Additional statistical tests are needed* - While more analysis is often valuable, the current statistical outputs (p-value and CI) are sufficient to interpret the **significance** and **precision** of the original finding. - Simply adding more tests without addressing the underlying **imprecision** (e.g., through larger studies) would not fundamentally change the interpretation of the current result. *The result shows no meaningful difference between groups* - The **p-value of 0.04** suggests there *is* a statistically significant difference, so stating there's "no meaningful difference" is incorrect based on the statistical evidence. - However, the **precision of the effect** with the large confidence interval means the *clinical meaningfulness* is still very uncertain, as the effect could be anywhere from small to quite large. *The result is both statistically and clinically significant* - The result is **statistically significant** (p=0.04). - However, the **wide confidence interval (0.5 to 4.2)** means the **clinical significance** is unknown, as the true effect size could be small (0.5) or large (4.2); it is not definitively "clinically significant."

Q: A randomized controlled trial of a new diabetes medication is stopped early when interim analysis shows significant benefit. The trial enrolled only 60% of the planned participants. What is the most important consideration about this early termination?

The result may overestimate the true treatment effect. ***The result may overestimate the true treatment effect*** - Early termination of a trial due to a strong interim signal of benefit often leads to an **overestimation of the treatment effect** observed in the final results. This phenomenon is known as **"stopping early for benefit bias"** or **"truncation bias"**. - This occurs because trials are stopped when random fluctuations happen to show a particularly large effect, and if the trial continued, the effect might regress to the mean. - This is the **most important methodological concern** when trials are terminated early based on interim analyses. *Early termination is always appropriate when benefit is shown* - While showing benefit is a common reason for early termination, it is not "always appropriate" without careful consideration of the **magnitude of benefit**, **potential harms**, and the impact on **statistical inference**. - **Ethical considerations** to prevent exposing more patients to an inferior treatment must be balanced against the **scientific rigor** of completing the study as planned. *Early termination violates research protocols* - **Research protocols** often include pre-specified rules for early termination based on interim analyses, such as a **stopping boundary** for efficacy or harm (e.g., O'Brien-Fleming boundaries). - If a protocol includes such rules, termination is within the protocol, not a violation. However, if no such rules are in place and termination is ad hoc, it could be problematic. *Statistical significance justifies stopping the trial* - While **statistical significance** is necessary for early stopping, it is not the sole justification. - **Clinical significance**, the magnitude of the effect, ethical considerations, and the potential for **overestimation bias** are also crucial factors in the decision to stop early.

Q: A screening test for a rare disease has 99% sensitivity and 95% specificity. In a population where the disease prevalence is 0.1%, what is the most important consideration for implementing this test?

Low prevalence will result in many false positives. ***Low prevalence will result in many false positives*** - In populations with **low disease prevalence**, even a test with high specificity can yield a large number of **false positives** because the vast majority of tested individuals do not have the condition. - For example, with a 95% specificity and 0.1% prevalence, 5% of healthy individuals will test positive, which is significantly more than the true positives from a 0.1% prevalence. - The **positive predictive value (PPV)** will be very low (~1.9%), meaning most positive results will be false positives. *High specificity ensures accurate results* - While high specificity (95%) means a low rate of false positives among healthy individuals, in a **rare disease** scenario, the absolute number of healthy people is very large, leading to many healthy individuals being incorrectly flagged. - This high number of false positives can lead to unnecessary follow-up procedures, anxiety, and resource strain, undermining the screening program's effectiveness. *High sensitivity makes it excellent for screening* - **High sensitivity** (99%) ensures that most true cases are identified, which is crucial for a good screening test. - However, in a low prevalence setting, high sensitivity alone does not address the issue of **false positives** among the vast majority of healthy individuals, which can overwhelm the system and cause patient harm. *The test performance is ideal for any population* - The performance of a screening test is highly dependent on the **prevalence of the disease** in the target population, especially the predictive values (PPV and NPV). - A test that performs well in a high-prevalence population may be unsuitable for screening a low-prevalence population due to a high rate of **false positives** and low PPV, making it impractical for population-wide screening.

Question 1

Which one of the following epidemiologic methods can be used to identify risk factors and estimate the degree of risk?

Accepted Answer

Case control and Cohort studies

Answer

Cohort study and Ecological studies

Answer

Case control and Cross-sectional studies

Answer

Cohort study and Randomized controlled trial

Question 2

What is the relative risk of developing pulmonary embolism in users of oral contraceptives as per the information given below?

Accepted Answer

4.80

Answer

0.24

Answer

0.48

Answer

2.40

Question 3

In a case-control study, 300 women aged 20-45 years suffering from breast cancer were compared with age-matched 300 women without breast cancer. It was observed that 120 women among cases and 60 women among controls were obese. The odds ratio of developing breast cancer among obese women is:

Accepted Answer

8/3

Answer

11/5

Answer

11/3

Answer

9/5

Question 4

Predictive accuracy of a screening test depends on the following EXCEPT:

Accepted Answer

Disease incidence

Answer

Specificity of screening test

Answer

Disease prevalence

Answer

Sensitivity of screening test

Question 5

What is the specificity of sputum microscopy in detection of Pulmonary Tuberculosis (PTB) as per the information given below?

Accepted Answer

90 %

Answer

94 %

Answer

10 %

Answer

36 %

Question 6

A research study comparing two groups shows a statistically significant difference (p=0.04) but the confidence interval is very wide (0.5 to 4.2). How should this result be interpreted?

Accepted Answer

The result is statistically significant but imprecise

Answer

Additional statistical tests are needed

Answer

The result shows no meaningful difference between groups

Answer

The result is both statistically and clinically significant

Question 7

A randomized controlled trial of a new diabetes medication is stopped early when interim analysis shows significant benefit. The trial enrolled only 60% of the planned participants. What is the most important consideration about this early termination?

Accepted Answer

The result may overestimate the true treatment effect

Answer

Early termination is always appropriate when benefit is shown

Answer

Early termination violates research protocols

Answer

Statistical significance justifies stopping the trial

Question 8

A depression screening tool developed in specialty clinics performs poorly when used in primary care settings. The positive predictive value drops from 85% to 25%. What is the most likely explanation?

Accepted Answer

Disease prevalence is lower in primary care than specialty clinics

Answer

The tool is fundamentally flawed

Answer

Patients in primary care are less honest about symptoms

Answer

Primary care physicians are using the tool incorrectly

Question 9

A cohort study follows 5,000 nurses for 20 years to study night shift work and breast cancer risk. After 10 years, 30% of participants have been lost to follow-up. What is the most significant threat to study validity?

Accepted Answer

Loss to follow-up may introduce bias if related to exposure or outcome

Answer

Twenty years is too long for meaningful results

Answer

Nurses are not representative of the general population

Answer

The study should be converted to a case-control design

Question 10

A screening test for a rare disease has 99% sensitivity and 95% specificity. In a population where the disease prevalence is 0.1%, what is the most important consideration for implementing this test?

Accepted Answer

Low prevalence will result in many false positives

Answer

High specificity ensures accurate results

Answer

High sensitivity makes it excellent for screening

Answer

The test performance is ideal for any population

Biostatistics — MCQs

Biostatistics — MCQs

On this page

Practice by Chapter

Want unlimited practice?