Biostatistics principles for clinical trials MCQs With Answer

Introduction

Biostatistics principles for clinical trials MCQs With Answer is a concise question bank designed for M.Pharm students preparing for exams in Clinical Research & Regulatory Requirements. This collection focuses on essential statistical concepts applied to clinical trials — hypothesis testing, errors and power, randomization strategies, survival analysis, sample size determination, handling multiplicity and missing data, interim analyses, and trial design types (non-inferiority, equivalence, crossover, cluster). Each multiple-choice question is crafted to reinforce both conceptual understanding and practical application in study design, analysis, and regulatory interpretation. Answers are provided for quick self-assessment and targeted revision to build competence for academic and professional roles in clinical research.

Q1. What does a p-value represent in the context of hypothesis testing in clinical trials?

• The probability that the null hypothesis is true given the observed data
• The probability of observing the data, or something more extreme, if the null hypothesis is true
• The probability that the alternative hypothesis is true given the observed data
• The probability of making a Type II error

Correct Answer: The probability of observing the data, or something more extreme, if the null hypothesis is true

Q2. In a clinical trial, a Type I error (alpha) is best described as:

• Failing to detect a true treatment effect
• Rejecting the null hypothesis when it is actually true
• Accepting the null hypothesis when the alternative is true
• Overestimating the treatment effect size

Correct Answer: Rejecting the null hypothesis when it is actually true

Q3. Statistical power of a trial is defined as:

• The probability of making a Type I error
• The probability of correctly rejecting the null hypothesis when the alternative is true
• The proportion of missing data that can be tolerated
• The expected p-value under the alternative hypothesis

Correct Answer: The probability of correctly rejecting the null hypothesis when the alternative is true

Q4. Which statement correctly distinguishes a two-sided test from a one-sided test?

• A two-sided test only detects increases in the primary endpoint
• A two-sided test assesses for a difference in either direction from the null value
• A one-sided test requires a larger sample size than a two-sided test for the same power
• A one-sided test evaluates equivalence while a two-sided test evaluates non-inferiority

Correct Answer: A two-sided test assesses for a difference in either direction from the null value

Q5. The intention-to-treat (ITT) principle in randomized clinical trials mandates:

• Excluding any subject who deviates from study protocol from the final analysis
• Analyzing participants according to the treatment actually received
• Including all randomized participants in the analysis according to their assigned groups
• Pooling results from different trials without adjustment

Correct Answer: Including all randomized participants in the analysis according to their assigned groups

Q6. Allocation concealment in randomization primarily aims to prevent:

• Performance bias after treatment allocation is revealed
• Bias in outcome assessment by blinded investigators
• Selection bias arising from foreknowledge of upcoming assignments
• Measurement error in laboratory assays

Correct Answer: Selection bias arising from foreknowledge of upcoming assignments

Q7. Which randomization method is optimal to ensure balance across important baseline prognostic factors?

• Simple randomization without restrictions
• Block randomization with a fixed block size only
• Stratified randomization with separate randomization lists within strata
• Cluster randomization by treatment center

Correct Answer: Stratified randomization with separate randomization lists within strata

Q8. The Kaplan-Meier estimator is used to:

• Estimate the mean of a normally distributed continuous endpoint
• Estimate the survival function or time-to-event probability while accounting for censoring
• Compare variances between two independent groups
• Adjust for multiple comparisons in interim analyses

Correct Answer: Estimate the survival function or time-to-event probability while accounting for censoring

Q9. The log-rank test in survival analysis is primarily used to:

• Adjust hazard ratios for covariates in a multivariable model
• Compare survival distributions between two or more groups
• Estimate median survival time for a single group
• Test proportional hazards assumption

Correct Answer: Compare survival distributions between two or more groups

Q10. A key assumption of the Cox proportional hazards model is that:

• Baseline hazard functions are identical across groups
• Hazard ratios between groups remain constant over time
• Event times follow a normal distribution
• There is no censoring of outcome data

Correct Answer: Hazard ratios between groups remain constant over time

Q11. In a non-inferiority clinical trial, the non-inferiority margin represents:

• The expected benefit of the new treatment over placebo
• The maximum clinically acceptable difference by which the new treatment may be worse than active control
• The minimum sample size required to demonstrate superiority
• The alpha spending function for interim looks

Correct Answer: The maximum clinically acceptable difference by which the new treatment may be worse than active control

Q12. An equivalence trial differs from a non-inferiority trial in that an equivalence trial:

• Aims to show the new treatment is superior to standard of care
• Uses a one-sided confidence interval to declare success
• Aims to show the treatment effect lies within pre-specified upper and lower bounds
• Does not require pre-specification of margins

Correct Answer: Aims to show the treatment effect lies within pre-specified upper and lower bounds

Q13. Interim analyses with potential early stopping for efficacy should use pre-specified boundaries because:

• They increase Type II error without adjustment
• Unplanned looks cannot affect Type I error
• They control overall Type I error inflation due to multiple looks at data
• They remove the need for final analysis

Correct Answer: They control overall Type I error inflation due to multiple looks at data

Q14. Multiplicity in clinical trials (multiple endpoints or comparisons) primarily increases the risk of:

• Type II error (false negatives)
• Type I error (false positives)
• Loss to follow-up
• Violation of randomization

Correct Answer: Type I error (false positives)

Q15. A 95% confidence interval for a treatment effect should be interpreted as:

• The probability that the true effect lies within this interval is 95%
• The treatment effect will be clinically significant if the interval excludes zero regardless of context
• The interval contains values that are compatible with the observed data under repeated sampling
• The p-value is guaranteed to be less than 0.05 if zero is outside the interval

Correct Answer: The interval contains values that are compatible with the observed data under repeated sampling

Q16. Which change will generally increase the required sample size for a superiority trial, holding other factors constant?

• Expecting a larger effect size between treatments
• Accepting a higher Type I error (larger alpha)
• Requiring higher statistical power (e.g., from 80% to 90%)
• Using a more liberal endpoint definition that decreases variability

Correct Answer: Requiring higher statistical power (e.g., from 80% to 90%)

Q17. When data are missing at random (MAR), which approach is generally preferred for handling missing outcome data?

• Complete-case analysis only using subjects with full data
• Last observation carried forward without modeling
• Multiple imputation that accounts for uncertainty in missing values
• Dropping the variable with missing data from analysis

Correct Answer: Multiple imputation that accounts for uncertainty in missing values

Q18. A per-protocol analysis in a randomized trial refers to:

• Analyzing all randomized participants regardless of adherence
• Analyzing only participants who completed the study according to the protocol
• Pooling data across trials in a meta-analysis
• Using intention-to-treat principles with additional imputation

Correct Answer: Analyzing only participants who completed the study according to the protocol

Q19. For cluster-randomized trials, the intra-cluster correlation coefficient (ICC) is important because it:

• Indicates the individual subject-level variance only
• Determines the degree of similarity of outcomes within clusters and affects effective sample size
• Can be ignored if cluster sizes are equal
• Directly replaces the need to randomize clusters

Correct Answer: Determines the degree of similarity of outcomes within clusters and affects effective sample size

Q20. The area under the ROC curve (AUC) for a diagnostic test represents:

• The proportion of true positives among all positive test results
• The probability that a randomly chosen diseased subject has a higher test value than a randomly chosen non-diseased subject
• The optimal cut-off value for the test
• The sensitivity multiplied by specificity

Correct Answer: The probability that a randomly chosen diseased subject has a higher test value than a randomly chosen non-diseased subject

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