Biostatistics Practice Questions

Q: A case-control study finds an odds ratio of 3.5 (95% CI: 2.1-5.8) for the association between smoking and lung cancer. However, the researchers did not control for occupational exposures, which are known risk factors for lung cancer. Analyze how this affects the interpretation of the results.

The association is likely overestimated due to confounding. ***The association is likely overestimated due to confounding*** - **Confounding occurs** when an unmeasured or uncontrolled factor (**occupational exposures**) is associated with both the exposure (smoking) and the outcome (lung cancer). - If occupational exposures are also a risk factor for lung cancer and are more common in smokers, the observed odds ratio for smoking would be **inflated**, suggesting a stronger association than truly exists. *The lack of control for occupational exposures has no impact* - This statement is incorrect because **occupational exposures are known risk factors for lung cancer** and could be associated with smoking status. - Ignoring such a factor violates the assumptions of a valid association measure, leading to a potentially **spurious association**. *The confidence interval indicates the results are not significant* - The 95% confidence interval (2.1-5.8) for the odds ratio **does not include 1.0**, which means the results are statistically significant. - A significant finding only implies that the observed association is unlikely due to **random chance**, not that it is free from bias. *The odds ratio is underestimated due to confounding* - For confounding to cause underestimation, occupational exposures would need to be negatively associated with smoking or positively associated with the lack of smoking. - Given that occupational exposures are additional risk factors for lung cancer and often correlate with smoking, **overestimation of the odds ratio** is a more probable outcome.

Q: A research study on a new diabetes medication reports the following: Control group event rate = 20%, Treatment group event rate = 15%, Relative risk = 0.75, Number needed to treat = 20. Analyze these results and determine what they indicate about the treatment's clinical significance.

The treatment provides modest clinical benefit. ***The treatment provides modest clinical benefit*** - The **relative risk of 0.75** indicates a 25% reduction in the event rate (1 - 0.75 = 0.25), while the **Number Needed to Treat (NNT) of 20** means that 20 patients need to be treated for one additional patient to benefit. - A NNT of 20 suggests a **modest benefit**; while not negligible, it isn't an overwhelmingly strong effect, implying that a reasonable number of patients must be treated to see one positive outcome. *The treatment provides substantial clinical benefit* - A substantial clinical benefit would typically be indicated by a much **lower NNT** (e.g., NNT < 10) and a larger relative risk reduction. - While there is a benefit, an NNT of 20 suggests it is not dramatic enough to be considered "substantial" for most clinical scenarios. *The results are contradictory and cannot be interpreted* - The provided results (control event rate, treatment event rate, relative risk, NNT) are **consistent with each other** and can be readily interpreted. - The calculations are standard epidemiological measures, and they convey a clear picture of the treatment's effect size. *The treatment has no clinically meaningful effect* - The **relative risk of 0.75** and an **NNT of 20** both clearly indicate a reduction in the event rate, meaning there is an effect. - An **effect size** that requires treating 20 patients to prevent one adverse event is generally considered meaningful, even if it's not large. *The treatment is harmful* - The **relative risk of 0.75** indicates a **reduction in events** in the treatment group compared to the control group, which signifies a beneficial effect. - If the treatment were harmful, the event rate in the treatment group would be higher, resulting in a relative risk greater than 1.

Q: A cross-sectional study surveys 5,000 adults about their exercise habits and measures their BMI at the same time. What is the main limitation of this study design?

It cannot determine temporal relationships. ***It cannot determine temporal relationships*** - A **cross-sectional study** collects data at a single point in time, making it impossible to determine which came first: the exercise habits or the BMI. - This limitation prevents the establishment of **cause-and-effect relationships**, as the temporal sequence of events cannot be ascertained. *It cannot measure multiple variables simultaneously* - This statement is incorrect; cross-sectional studies are perfectly capable of measuring **multiple variables** (e.g., exercise habits, BMI, age, gender) at the same time. - The purpose of such studies often involves examining **associations** between various factors present concurrently. *It is too expensive to conduct* - Cross-sectional studies are generally **less expensive** and quicker to conduct compared to longitudinal studies, as they don't require follow-up over time. - Therefore, cost is typically considered an advantage, not a major limitation, especially for large sample sizes. *It cannot establish prevalence of obesity* - This statement is incorrect; a cross-sectional study is ideal for estimating the **prevalence** of a condition (like obesity) within a population at a specific time. - By measuring BMI in 5,000 adults, the study can directly calculate the proportion of individuals classified as obese.

Q: A clinical trial comparing two medications reports a relative risk of 0.75 (95% CI: 0.60-0.90) for the primary outcome. How should this result be interpreted?

The new medication reduces risk by 25%. ***The new medication reduces risk by 25%*** - A **relative risk (RR) of 0.75** means that the risk in the exposed group is 75% of the risk in the unexposed group, indicating a **25% reduction in risk** (1 - 0.75 = 0.25). - The **95% confidence interval (CI) of 0.60-0.90** does not include 1, signifying that the observed risk reduction is **statistically significant**. *The confidence interval is too wide to be meaningful* - A CI of 0.60-0.90 is **relatively narrow** in this context, providing a precise estimate of the treatment effect. - The fact that the entire CI is **below 1** indicates a meaningful and statistically significant reduction in risk. *The new medication increases risk by 25%* - This interpretation would be correct if the **relative risk were 1.25** (i.e., 25% *increase* in risk). - An RR of 0.75 indicates a **reduction**, not an increase, in risk. *The new medication reduces risk by 75%* - A 75% reduction in risk would correspond to a **relative risk of 0.25** (1 - 0.75 = 0.25). - The observed relative risk of 0.75 signifies a **25% risk reduction**.

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.

Question 1

A randomized controlled trial of a new antidepressant shows statistically significant improvement (p = 0.02) compared to placebo. However, the effect size is small (Cohen's d = 0.2), and the confidence interval barely excludes the null. Analyze the clinical implications of these findings.

Accepted Answer

The treatment shows minimal clinical benefit despite statistical significance

Answer

The results are contradictory and cannot be interpreted

Answer

The treatment should be immediately adopted due to statistical significance

Answer

The small effect size invalidates the statistical significance

Question 2

A cohort study follows 10,000 nurses for 20 years to study the relationship between night shift work and breast cancer. The study finds a hazard ratio of 1.3 (95% CI: 1.1-1.6). However, 30% of participants were lost to follow-up. Analyze how this affects the validity of the results.

Accepted Answer

The loss to follow-up may introduce bias depending on reasons for dropout

Answer

The hazard ratio is likely underestimated

Answer

The results are invalid due to high loss to follow-up

Answer

The confidence interval accounts for the loss to follow-up

Question 3

A case-control study finds an odds ratio of 3.5 (95% CI: 2.1-5.8) for the association between smoking and lung cancer. However, the researchers did not control for occupational exposures, which are known risk factors for lung cancer. Analyze how this affects the interpretation of the results.

Accepted Answer

The association is likely overestimated due to confounding

Answer

The lack of control for occupational exposures has no impact

Answer

The confidence interval indicates the results are not significant

Answer

The odds ratio is underestimated due to confounding

Question 4

A research study on a new diabetes medication reports the following: Control group event rate = 20%, Treatment group event rate = 15%, Relative risk = 0.75, Number needed to treat = 20. Analyze these results and determine what they indicate about the treatment's clinical significance.

Accepted Answer

The treatment provides modest clinical benefit

Answer

The treatment provides substantial clinical benefit

Answer

The results are contradictory and cannot be interpreted

Answer

The treatment has no clinically meaningful effect

Question 5

A cross-sectional study surveys 5,000 adults about their exercise habits and measures their BMI at the same time. What is the main limitation of this study design?

Accepted Answer

It cannot determine temporal relationships

Answer

It cannot measure multiple variables simultaneously

Answer

It is too expensive to conduct

Answer

It cannot establish prevalence of obesity

Question 6

A clinical trial comparing two medications reports a relative risk of 0.75 (95% CI: 0.60-0.90) for the primary outcome. How should this result be interpreted?

Accepted Answer

The new medication reduces risk by 25%

Answer

The confidence interval is too wide to be meaningful

Answer

The new medication increases risk by 25%

Answer

The new medication reduces risk by 75%

Question 7

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 8

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 9

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 10

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%

Biostatistics — MCQs

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