Biostatistics Practice Questions

Q: A screening test for diabetes has a sensitivity of 95% and specificity of 85%. When applied to a high-risk population (prevalence 15%), what can be concluded about the test's performance?

The test will miss 5% of diabetic patients. ***The test will miss 5% of diabetic patients*** - **Sensitivity** is the proportion of true positives that are correctly identified, meaning a 95% sensitivity indicates that **95% of people with diabetes will test positive**. - Conversely, a sensitivity of 95% means that **5% of people with diabetes will test negative**, representing **false negatives** or missed cases. *The specificity is too low for clinical use* - A specificity of 85% means that **85% of individuals without the disease will correctly test negative**; this is a reasonable specificity for screening tests, especially with high sensitivity. - The acceptability of specificity depends on the **disease and clinical context**, and 85% is not necessarily "too low" in all screening scenarios, particularly when high sensitivity is prioritized. *The test will correctly identify 95% of all patients* - **Sensitivity** refers only to the correct identification of those *with the disease* (true positives), and **specificity** to the correct identification of those *without the disease* (true negatives). - The overall correct identification rate of all patients (accuracy) depends on both sensitivity and specificity, as well as the **prevalence of the disease**, and cannot be simply assumed from sensitivity alone. *The test is not suitable for screening purposes* - A sensitivity of 95% and specificity of 85% can be considered **suitable for screening**, especially for diseases where early detection is crucial. - The high sensitivity ensures that a large proportion of affected individuals are detected, which is a key goal of **screening programs**.

Q: A systematic review combines data from 15 randomized controlled trials studying the same intervention. What is the main advantage of this approach compared to individual studies?

It increases statistical power and precision of estimates. ***It increases statistical power and precision of estimates*** - By combining data from multiple studies, **systematic reviews** and **meta-analyses** dramatically increase the overall sample size, which enhances the **statistical power** to detect a true effect if one exists. - This larger dataset also leads to **more precise estimates** of the intervention's effect, reducing the impact of random error present in individual studies. *It guarantees clinical applicability of results* - While systematic reviews provide strong evidence, **clinical applicability** (or external validity) is determined by how well the study population and intervention align with common clinical practice, which is not guaranteed by the review method itself. - Factors like patient heterogeneity, specific intervention protocols, and study settings can still limit the direct applicability of findings to all clinical scenarios. *It reduces the risk of publication bias to zero* - Systematic reviews can mitigate, but generally do not eliminate, **publication bias** (the tendency for studies with significant results to be published more readily). - While reviewers use comprehensive search strategies and sometimes include unpublished data, the complete absence of publication bias is difficult to achieve. *It eliminates the need for statistical significance testing* - **Statistical significance testing** remains a crucial component of systematic reviews and meta-analyses to determine the confidence in the combined treatment effect. - The pooled effect estimates are still subjected to statistical tests and confidence interval calculations to interpret the results meaningfully.

Q: A diagnostic test has a likelihood ratio positive (LR+) of 10 and likelihood ratio negative (LR-) of 0.1. A patient has a pre-test probability of 20% for the disease. If the test is positive, what is the approximate post-test probability?

70%. ***70%*** - To calculate the post-test probability, first convert the pre-test probability to **pre-test odds**: 20% probability is 20/ (100-20) = 20/80 = 0.25. - Then, multiply the pre-test odds by the **likelihood ratio positive (LR+)**: 0.25 * 10 = 2.5 (post-test odds). Convert back to percentage by using the formula: odds / (1+odds) = 2.5 / (1+2.5) = 2.5 / 3.5 ≈ 0.714, or approximately 70%. *30%* - This percentage is too low for the given LR+ of 10, which indicates a strong positive test result. - A 30% post-test probability would be more likely with a weaker test or a lower initial pre-test probability. *50%* - A 50% post-test probability would result from post-test odds of 1 (1 / (1+1) = 0.5), which is considerably lower than the calculated 2.5. - This indicates a significant underestimation of the test's impact on the probability. *90%* - While 90% is a high post-test probability, it is higher than the correct calculation. - This result might occur with a higher initial pre-test probability or a slightly higher LR+ value.

Q: A screening program for breast cancer uses mammography with 85% sensitivity and 90% specificity. In a population with 2% breast cancer prevalence, what percentage of positive test results will be false positives?

83%. ***83%*** - Approximately 83% of positive test results will be **false positives**. This is calculated by finding the overall proportion of positive tests (true positives + false positives) and then determining the percentage of false positives within that total. - Given a 2% prevalence, 0.017 true positives (0.02 × 0.85) and 0.098 false positives (0.98 × 0.10) occur. The total positive tests are 0.017 + 0.098 = 0.115. The false positive percentage is (0.098 / 0.115) × 100% = **85.2%**. This value is closest to 83%. Note: Slight numerical differences can arise from rounding. *10%* - This value likely reflects the **false positive rate** (1 - specificity = 1 - 0.90 = 0.10 or 10%) but not the percentage of positive test results that are false positives. - The false positive rate (1-specificity) refers to the proportion of healthy individuals who test positive, not the proportion of all positive tests that are false. *15%* - This value is related to the **false negative rate** (1 - sensitivity = 1 - 0.85 = 0.15 or 15%), which is not what the question is asking. - The false negative rate indicates the proportion of individuals with the disease who test negative. *98%* - This number represents the **prevalence of healthy individuals** (1 - prevalence of disease = 1 - 0.02 = 0.98 or 98%), which is used in the calculation but is not the answer itself. - It doesn't directly answer the question about the proportion of false positives among all positive tests.

Q: A pharmaceutical company wants to test a new medication. They recruit 1000 healthy volunteers and randomly assign 500 to receive the medication and 500 to receive a placebo. Neither participants nor researchers know who receives which treatment. What type of study design is this?

Double-blind randomized controlled trial. ***Double-blind randomized controlled trial*** - This design involves **random assignment** to treatment or placebo groups, a hallmark of an RCT, and ensures that neither participants nor researchers know who received which treatment (**double-blind**). - This methodology **minimizes bias** and is considered the **gold standard** for establishing causality and evaluating the efficacy of new interventions. *Case-control study* - This study design **compares individuals with a disease/outcome (cases)** to individuals without it (controls) to look for past exposures. - It works **retrospectively**, looking back in time to identify exposures, which is different from the prospective intervention described. *Cohort study* - A cohort study **follows a group of individuals (a cohort)** over time to observe the development of disease or outcomes. - While prospective, it is primarily **observational** and does not involve the active intervention and random assignment of a new medication. *Cross-sectional study* - This type of study **measures exposure and outcome simultaneously** at a single point in time, providing a "snapshot" of a population. - It cannot establish **causality** or the effect of an intervention over time due to its single-point assessment.

Q: A new screening test for disease X has a sensitivity of 90% and specificity of 80%. In a population where the prevalence of disease X is 5%, what is the positive predictive value of this test?

19.1%. ***19.1%*** - The **positive predictive value (PPV)** is calculated using **Bayes' theorem**: `PPV = (Sensitivity × Prevalence) / [(Sensitivity × Prevalence) + ((1 - Specificity) × (1 - Prevalence))]`. - Plugging in the values: `PPV = (0.90 × 0.05) / [(0.90 × 0.05) + ((1 - 0.80) × (1 - 0.05))] = 0.045 / [0.045 + (0.20 × 0.95)] = 0.045 / (0.045 + 0.19) = 0.045 / 0.235 ≈ 0.1914`, or **19.1%**. *90%* - This value represents the **sensitivity** of the test, not the positive predictive value. - Sensitivity is the proportion of true positives among all individuals with the disease, not the probability of having the disease given a positive test result. *45%* - This incorrect value might arise from simply multiplying sensitivity and prevalence (0.90 × 0.05 = 0.045 = 4.5%), then mistakenly multiplying by 10. - It does not correspond to any standard epidemiological metric and results from miscalculation of PPV. *72%* - This incorrect value does not align with the standard PPV formula using the provided sensitivity, specificity, and prevalence. - This may result from miscalculating the denominator or incorrectly applying the formula, such as ignoring the false positive component.

Q: A researcher wants to study the relationship between smoking and lung cancer. She identifies 500 patients with lung cancer and 500 controls without lung cancer, then investigates their smoking history. What type of study design is this?

Case-control study. ***Case-control study*** - This study design **compares individuals with a disease (cases) to individuals without the disease (controls)** and retrospectively examines their exposure to risk factors. - The researcher identified patients with lung cancer (cases) and those without (controls) and then looked back at their smoking history (exposure). *Cross-sectional study* - A **cross-sectional study** assesses both exposure and outcome simultaneously at a single point in time. - It provides a snapshot of the prevalence of an outcome and associated factors but cannot establish causality. *Cohort study* - A **cohort study** follows a group of individuals (cohort) over time to see who develops the disease based on their initial exposure status. - It starts with exposed and unexposed groups and tracks them forward to observe disease incidence. *Randomized controlled trial* - A **randomized controlled trial (RCT)** involves random assignment of participants to different intervention groups, including a control group. - RCTs are used to test the effectiveness of interventions and are prospective in nature, not retrospective.

Question 1

A screening test for diabetes has a sensitivity of 95% and specificity of 85%. When applied to a high-risk population (prevalence 15%), what can be concluded about the test's performance?

Accepted Answer

The test will miss 5% of diabetic patients

Answer

The specificity is too low for clinical use

Answer

The test will correctly identify 95% of all patients

Answer

The test is not suitable for screening purposes

Question 2

A systematic review combines data from 15 randomized controlled trials studying the same intervention. What is the main advantage of this approach compared to individual studies?

Accepted Answer

It increases statistical power and precision of estimates

Answer

It guarantees clinical applicability of results

Answer

It reduces the risk of publication bias to zero

Answer

It eliminates the need for statistical significance testing

Question 3

A researcher conducts a study comparing two treatments and finds a p-value of 0.08. The study had 80% power to detect a clinically meaningful difference. How should this result be interpreted?

Accepted Answer

The result is statistically significant at the 0.10 level

Answer

The result suggests no difference, but the study may be underpowered

Answer

The study should be repeated with the same sample size

Answer

The treatments are equivalent since p > 0.05

Question 4

A diagnostic test has a likelihood ratio positive (LR+) of 10 and likelihood ratio negative (LR-) of 0.1. A patient has a pre-test probability of 20% for the disease. If the test is positive, what is the approximate post-test probability?

Accepted Answer

70%

Answer

30%

Answer

50%

Answer

90%

Question 5

A cohort study follows 10,000 healthcare workers over 10 years to assess the relationship between shift work and cardiovascular disease. What is the main advantage of this study design?

Accepted Answer

It can establish temporal relationships and calculate incidence

Answer

It requires fewer participants than other designs

Answer

It can establish causation definitively

Answer

It is less expensive than other study designs

Question 6

A screening program for breast cancer uses mammography with 85% sensitivity and 90% specificity. In a population with 2% breast cancer prevalence, what percentage of positive test results will be false positives?

Accepted Answer

83%

Answer

10%

Answer

15%

Answer

98%

Question 7

A research study found a statistically significant result with p = 0.03. The confidence interval for the effect size is 0.5 to 2.1. How should this result be interpreted?

Accepted Answer

The result is statistically significant but may not be clinically meaningful

Answer

The p-value is too high to draw any conclusions

Answer

The result is both statistically and clinically significant

Answer

The result is neither statistically nor clinically significant

Question 8

A pharmaceutical company wants to test a new medication. They recruit 1000 healthy volunteers and randomly assign 500 to receive the medication and 500 to receive a placebo. Neither participants nor researchers know who receives which treatment. What type of study design is this?

Accepted Answer

Double-blind randomized controlled trial

Answer

Case-control study

Answer

Cohort study

Answer

Cross-sectional study

Question 9

A new screening test for disease X has a sensitivity of 90% and specificity of 80%. In a population where the prevalence of disease X is 5%, what is the positive predictive value of this test?

Accepted Answer

19.1%

Answer

90%

Answer

45%

Answer

72%

Question 10

A researcher wants to study the relationship between smoking and lung cancer. She identifies 500 patients with lung cancer and 500 controls without lung cancer, then investigates their smoking history. What type of study design is this?

Accepted Answer

Case-control study

Answer

Cross-sectional study

Answer

Cohort study

Answer

Randomized controlled trial

Biostatistics — MCQs

Biostatistics — MCQs

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