Biostatistics Practice Questions

Q: A screening program for colorectal cancer uses fecal occult blood testing (FOBT) with 70% sensitivity and 95% specificity. When positive, patients undergo colonoscopy. Analyze the strategy's effectiveness in a population with 3% colorectal cancer prevalence.

The strategy would result in too many unnecessary colonoscopies. ***The strategy would result in too many unnecessary colonoscopies*** - With a 3% prevalence and 95% specificity, a significant number of healthy individuals (false positives) will be referred for **invasive and costly colonoscopies**. - For every 10,000 people, roughly 485 healthy individuals (9,700 × 5%) would have a positive FOBT, compared to only 210 true positives (300 × 70%). - This yields a **positive predictive value of only ~30%**, meaning most positive results lead to unnecessary procedures. *The strategy is optimal for this population* - This statement is incorrect because the high number of **false positives** leading to unnecessary invasive procedures makes the strategy suboptimal for a low-prevalence population. - An optimal strategy would balance the benefits of early detection with the risks and costs of follow-up diagnostics. *The strategy has limited effectiveness due to low sensitivity* - While a sensitivity of 70% means 30% of actual cancer cases (90 out of 300) would be missed as **false negatives**, this is actually acceptable for a screening test. - However, the primary issue in a low-prevalence population is the burden of **false positives**, not the sensitivity limitation. *The strategy is highly effective due to high specificity* - High specificity (95%) is generally good, but in a low-prevalence population (3%), even a small percentage of false positives translates into a large absolute number. - For every 10,000 individuals, 485 healthy people would receive a positive result, leading to **unnecessary follow-up** procedures that outweigh the 210 true positives detected.

Q: 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.

The treatment shows minimal clinical benefit despite statistical significance. **The treatment shows minimal clinical benefit despite statistical significance** - A **p-value of 0.02** indicates statistical significance, meaning the observed difference is unlikely due to random chance, but it does not convey the magnitude or importance of the effect. - A **small effect size (Cohen's d = 0.2)** suggests a **clinically insignificant difference**, even if statistically detectable; the treatment's impact on patient outcomes is likely very minor. *The results are contradictory and cannot be interpreted* - The results are **not contradictory** but rather highlight the distinction between **statistical significance** and **clinical significance**, which is a common scenario in research. - We can interpret these findings by understanding that a statistically significant result with a small effect size means the intervention had an effect, but that effect is of **little practical importance**. *The treatment should be immediately adopted due to statistical significance* - Immediate adoption based solely on **statistical significance** is inappropriate when the **effect size is small** and the confidence interval barely excludes the null, as the clinical utility is questionable. - **Clinical relevance** and **effect size** are crucial considerations for adoption, as a treatment with minimal benefit may not justify its cost, potential side effects, or logistical challenges. *The small effect size invalidates the statistical significance* - **Statistical significance** and **effect size** are distinct concepts; a small effect size does not invalidate a statistically significant p-value. - The p-value tells us about the **probability of observing the data** if the null hypothesis were true, while the effect size quantifies the **magnitude of the observed effect**.

Q: 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.

The loss to follow-up may introduce bias depending on reasons for dropout. ***The loss to follow-up may introduce bias depending on reasons for dropout*** - A significant **loss to follow-up (30%)** can lead to **selection bias** if the reasons for dropout are related to both the exposure (night shift work) and the outcome (breast cancer). - For example, if nurses with higher risk or early symptoms of breast cancer are more likely to drop out, the observed hazard ratio could be either underestimated or overestimated. *The hazard ratio is likely underestimated* - It's not definitively true that the hazard ratio is underestimated; the direction of bias depends on whether those lost to follow-up had a higher or lower incidence of the outcome. - If nurses who developed breast cancer or had risk factors for it were more likely to drop out, the hazard ratio for the remaining cohort might be underestimated. Conversely, if healthier nurses dropped out, it could be overestimated. *The results are invalid due to high loss to follow-up* - While a 30% loss to follow-up is substantial and raises concerns about **validity**, it doesn't automatically invalidate the entire study. - The impact depends on the characteristics of those lost and the methods used to address missing data; sensitivity analyses might still provide useful insights. *The confidence interval accounts for the loss to follow-up* - The **confidence interval** primarily reflects the **precision** of the estimate, taking into account random error and sample size, not systematic biases introduced by loss to follow-up. - While a smaller effective sample size due to loss to follow-up might widen the CI, it does not correct for potential **selection bias**.

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.

Question 1

A case-control study of breast cancer finds that women with the disease are more likely to recall family history of cancer than healthy controls. This could represent recall bias. Analyze how this bias would affect the study's conclusions.

Accepted Answer

Recall bias would overestimate the association between family history and breast cancer

Answer

Recall bias would not affect the study results

Answer

Recall bias can be corrected through statistical adjustment

Answer

Recall bias would underestimate the association between family history and breast cancer

Question 2

A pharmaceutical company conducts a clinical trial comparing their new drug to an older, less effective treatment rather than current standard of care. The study shows superiority to the older treatment. Analyze the ethical and scientific issues with this study design.

Accepted Answer

The study is unethical because it doesn't use the best available control

Answer

The study design is scientifically valid regardless of control choice

Answer

The study design is appropriate for regulatory approval

Answer

The study should be accepted if patients consented to participation

Question 3

A clinical trial is stopped early after interim analysis shows the new treatment is superior to standard care (p = 0.001). However, only 60% of planned enrollment was completed. Analyze the implications of early trial termination.

Accepted Answer

Early termination may lead to overestimation of treatment effect

Answer

The trial should continue to planned completion regardless of interim results

Answer

The p-value is too low to be clinically meaningful

Answer

Early termination is justified by the strong statistical evidence

Question 4

A meta-analysis of 20 studies shows heterogeneity (I² = 85%) in results examining the effectiveness of a new treatment. The overall effect size is statistically significant (p < 0.01). Analyze the implications of this heterogeneity for clinical practice.

Accepted Answer

The heterogeneity suggests the treatment effect varies across populations

Answer

The statistical significance overrides concerns about heterogeneity

Answer

The heterogeneity indicates poor study quality

Answer

The meta-analysis should include more studies to reduce heterogeneity

Question 5

A screening program for colorectal cancer uses fecal occult blood testing (FOBT) with 70% sensitivity and 95% specificity. When positive, patients undergo colonoscopy. Analyze the strategy's effectiveness in a population with 3% colorectal cancer prevalence.

Accepted Answer

The strategy would result in too many unnecessary colonoscopies

Answer

The strategy is optimal for this population

Answer

The strategy has limited effectiveness due to low sensitivity

Answer

The strategy is highly effective due to high specificity

Question 6

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 7

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 8

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 9

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 10

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

Biostatistics — MCQs

Biostatistics — MCQs

On this page

Practice by Chapter

Want unlimited practice?