Biostatistics Practice Questions

Q: A meta-analysis of 10 studies shows significant benefit from a new treatment (p=0.001), but there is high heterogeneity between studies (I² = 78%). Individual studies show conflicting results. How should this evidence be interpreted?

Inconclusive evidence requiring additional studies. ***Inconclusive evidence requiring additional studies*** - High **heterogeneity (I² = 78%)** indicates that the studies are measuring different effects, making a combined statistical result unreliable even with a low p-value. - **Conflicting individual study results** despite a statistically significant meta-analysis result means that the overall conclusion might be misleading or only applicable to a specific, unidentifiable subgroup. *Evidence supports treatment in all patient populations* - **High heterogeneity** suggests that treatment effects vary significantly between studies, making it inappropriate to universally apply the findings to all patient populations. - The presence of **conflicting individual results** contradicts the idea of a universal benefit across all populations. *Statistical significance overrides heterogeneity concerns* - While a **low p-value (p=0.001)** indicates statistical significance for the overall effect, **high heterogeneity (I² = 78%)** fundamentally questions the validity of pooling such diverse results. - Ignoring significant heterogeneity can lead to **misleading conclusions** about the consistency and generalizability of the treatment effect. *Treatment harmful due to conflicting individual results* - While conflicting results do raise concerns about the consistency of the treatment effect, they do not automatically imply that the treatment is **harmful**. - The data merely indicates **inconsistency** rather than a detrimental effect, suggesting a need for further investigation to understand the variability.

Q: A clinical trial shows that a new cardiac medication reduces mortality from 20% to 15%. The relative risk is 0.75 and the number needed to treat is 20. How should the clinical significance be interpreted?

Modest benefit requiring individual risk-benefit assessment. ***Modest benefit requiring individual risk-benefit assessment*** - A reduction in mortality from 20% to 15% represents an absolute risk reduction of 5% (20% - 15% = 5%). This corresponds to a **Number Needed to Treat (NNT)** of 20 (1/0.05 = 20), meaning 20 patients must be treated to prevent one additional death. - While any reduction in mortality is beneficial, an NNT of 20 suggests a **modest benefit** rather than a dramatic one, necessitating an assessment of potential side effects, cost, and patient preferences to determine the overall utility. *Not clinically meaningful despite statistical significance* - A 5% absolute reduction in mortality and an NNT of 20 for a life-threatening condition like heart disease is generally considered **clinically meaningful**, as it directly impacts patient survival. - The implication of "statistical significance" without clinical meaning usually applies when the effect size is very small, which is not the case here given the mortality outcome. *Highly significant due to mortality reduction* - Although the medication reduces mortality, a **5% absolute risk reduction** and an **NNT of 20** are not typically considered "highly significant" in the context of immediately revolutionizing clinical practice. - "Highly significant" would imply a much larger reduction in adverse outcomes or a much lower NNT (e.g., NNT of 2-5). *Excellent result warranting immediate adoption* - An "excellent result" would imply a more substantial impact (e.g., a significantly lower NNT or a much larger absolute risk reduction) that clearly outweighs potential harms and costs for most patients. - The need to treat 20 patients to save one life suggests that while beneficial, the medication may not be universally suitable without considering individual patient factors and potential side effects in comparison to its benefits.

Q: A surgeon notices that their complication rates are higher than expected. Hospital administration suggests this might be due to taking more complex cases. Analyze the statistical concept that explains this phenomenon.

Confounding by case complexity. ***Confounding by case complexity*** - The higher complication rates are explained by the increased inherent risk associated with **more complex cases**, which the surgeon is undertaking. The complexity of cases is acting as a **confounding variable**, influencing both the surgeon's choice of cases and the complication rate. - To accurately assess the surgeon's performance, the analysis must account for the baseline risk disparities between patient groups, rather than simply comparing crude complication rates. *Observer bias in outcome assessment* - This bias occurs when the **observation or recording of outcomes** is systematically influenced by the observer's expectations or knowledge, not by the true characteristics of the cases themselves. - In this scenario, the issue is not how complications are observed, but rather the underlying patient population driving the observed rates. *Selection bias in case assignment* - Selection bias occurs when there are **systematic differences** between participant groups in a study that can distort the results. While the surgeon is "selecting" more complex cases, the term "selection bias" in statistical research typically refers to issues in forming comparative groups (e.g., in clinical trials). - Here, the issue is not a flaw in study design for comparison, but rather a characteristic of clinical practice where the surgeon's case mix inherently impacts outcomes. *Regression to the mean* - This statistical phenomenon describes the tendency for **extreme measurements** to be followed by less extreme measurements closer to the average. For example, an exceptionally high or low score is likely to be closer to the average on subsequent measurements. - While it explains why an unusually high measurement might decrease over time, it does not explain why consistently taking on more complex cases would lead to higher complication rates as a sustained trend.

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.

Question 1

A meta-analysis of 10 studies shows significant benefit from a new treatment (p=0.001), but there is high heterogeneity between studies (I² = 78%). Individual studies show conflicting results. How should this evidence be interpreted?

Accepted Answer

Inconclusive evidence requiring additional studies

Answer

Evidence supports treatment in all patient populations

Answer

Statistical significance overrides heterogeneity concerns

Answer

Treatment harmful due to conflicting individual results

Question 2

A clinical trial shows that a new cardiac medication reduces mortality from 20% to 15%. The relative risk is 0.75 and the number needed to treat is 20. How should the clinical significance be interpreted?

Accepted Answer

Modest benefit requiring individual risk-benefit assessment

Answer

Not clinically meaningful despite statistical significance

Answer

Highly significant due to mortality reduction

Answer

Excellent result warranting immediate adoption

Question 3

A researcher wants to study a new antidepressant in patients with severe depression. Standard antidepressants are available and effective. The pharmaceutical company insists on a placebo-controlled design, arguing it provides the clearest evidence of efficacy. What is the most appropriate study design?

Accepted Answer

Active-controlled trial comparing to standard antidepressant

Answer

Placebo-controlled trial as requested by the company

Answer

Crossover design with placebo and active drug periods

Answer

Observational study without intervention

Question 4

A screening program implements two tests in parallel (positive if either test is positive) for a disease. Test A has 80% sensitivity and 90% specificity. Test B has 70% sensitivity and 95% specificity. Analyze the expected performance of the combined approach.

Accepted Answer

Combined sensitivity will be higher but specificity will be lower

Answer

Both sensitivity and specificity will be higher

Answer

Combined sensitivity will be lower than either individual test

Answer

Combined specificity will be higher but sensitivity will be lower

Question 5

A surgeon notices that their complication rates are higher than expected. Hospital administration suggests this might be due to taking more complex cases. Analyze the statistical concept that explains this phenomenon.

Accepted Answer

Confounding by case complexity

Answer

Observer bias in outcome assessment

Answer

Selection bias in case assignment

Answer

Regression to the mean

Question 6

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 7

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 8

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 9

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 10

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

Biostatistics — MCQs

Biostatistics — MCQs

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