Power calculations for subgroup analyses US Medical PG Practice Questions and MCQs
Practice US Medical PG questions for Power calculations for subgroup analyses. These multiple choice questions (MCQs) cover important concepts and help you prepare for your exams.
Power calculations for subgroup analyses US Medical PG Question 1: A research team develops a new monoclonal antibody checkpoint inhibitor for advanced melanoma that has shown promise in animal studies as well as high efficacy and low toxicity in early phase human clinical trials. The research team would now like to compare this drug to existing standard of care immunotherapy for advanced melanoma. The research team decides to conduct a non-randomized study where the novel drug will be offered to patients who are deemed to be at risk for toxicity with the current standard of care immunotherapy, while patients without such risk factors will receive the standard treatment. Which of the following best describes the level of evidence that this study can offer?
- A. Level 1
- B. Level 3 (Correct Answer)
- C. Level 5
- D. Level 4
- E. Level 2
Power calculations for subgroup analyses Explanation: ***Level 3***
- A **non-randomized controlled trial** like the one described, where patient assignment to treatment groups is based on specific characteristics (risk of toxicity), falls into Level 3 evidence.
- This level typically includes **non-randomized controlled trials** and **well-designed cohort studies** with comparison groups, which are prone to selection bias and confounding.
- The study compares two treatments but lacks randomization, making it Level 3 evidence.
*Level 1*
- Level 1 evidence is the **highest level of evidence**, derived from **systematic reviews and meta-analyses** of multiple well-designed randomized controlled trials or large, high-quality randomized controlled trials.
- The described study is explicitly stated as non-randomized, ruling out Level 1.
*Level 2*
- Level 2 evidence involves at least one **well-designed randomized controlled trial** (RCT) or **systematic reviews** of randomized trials.
- The current study is *non-randomized*, which means it cannot be classified as Level 2 evidence, as randomization is a key criterion for this level.
*Level 4*
- Level 4 evidence includes **case series**, **case-control studies**, and **poorly designed cohort or case-control studies**.
- While the study is non-randomized, it is a controlled comparative trial rather than a case series or retrospective case-control study, placing it at Level 3.
*Level 5*
- Level 5 evidence is the **lowest level of evidence**, typically consisting of **expert opinion** without explicit critical appraisal, or based on physiology, bench research, or animal studies.
- While the drug was initially tested in animal studies, the current human comparative study offers a higher level of evidence than expert opinion or preclinical data.
Power calculations for subgroup analyses US Medical PG Question 2: You are conducting a study comparing the efficacy of two different statin medications. Two groups are placed on different statin medications, statin A and statin B. Baseline LDL levels are drawn for each group and are subsequently measured every 3 months for 1 year. Average baseline LDL levels for each group were identical. The group receiving statin A exhibited an 11 mg/dL greater reduction in LDL in comparison to the statin B group. Your statistical analysis reports a p-value of 0.052. Which of the following best describes the meaning of this p-value?
- A. There is a 95% chance that the difference in reduction of LDL observed reflects a real difference between the two groups
- B. Though A is more effective than B, there is a 5% chance the difference in reduction of LDL between the two groups is due to chance
- C. If 100 permutations of this experiment were conducted, 5 of them would show similar results to those described above
- D. This is a statistically significant result
- E. There is a 5.2% chance of observing a difference in reduction of LDL of 11 mg/dL or greater even if the two medications have identical effects (Correct Answer)
Power calculations for subgroup analyses Explanation: **There is a 5.2% chance of observing a difference in reduction of LDL of 11 mg/dL or greater even if the two medications have identical effects**
- The **p-value** represents the probability of observing results as extreme as, or more extreme than, the observed data, assuming the **null hypothesis** is true (i.e., there is no true difference between the groups).
- A p-value of 0.052 means there's approximately a **5.2% chance** that the observed 11 mg/dL difference (or a more substantial difference) occurred due to **random variation**, even if both statins were equally effective.
*There is a 95% chance that the difference in reduction of LDL observed reflects a real difference between the two groups*
- This statement is an incorrect interpretation of the p-value; it confuses the p-value with the **probability that the alternative hypothesis is true**.
- A p-value does not directly tell us the probability that the observed difference is "real" or due to the intervention being studied.
*Though A is more effective than B, there is a 5% chance the difference in reduction of LDL between the two groups is due to chance*
- This statement implies that Statin A is more effective, which cannot be concluded with a p-value of 0.052 if the significance level (alpha) was set at 0.05.
- While it's true there's a chance the difference is due to chance, claiming A is "more effective" based on this p-value before statistical significance is usually declared is misleading.
*If 100 permutations of this experiment were conducted, 5 of them would show similar results to those described above*
- This is an incorrect interpretation. The p-value does not predict the outcome of repeated experiments in this manner.
- It refers to the **probability under the null hypothesis in a single experiment**, not the frequency of results across multiple hypothetical repetitions.
*This is a statistically significant result*
- A p-value of 0.052 is generally considered **not statistically significant** if the conventional alpha level (significance level) is set at 0.05 (or 5%).
- For a result to be statistically significant at alpha = 0.05, the p-value must be **less than 0.05**.
Power calculations for subgroup analyses US Medical PG Question 3: A 25-year-old man with a genetic disorder presents for genetic counseling because he is concerned about the risk that any children he has will have the same disease as himself. Specifically, since childhood he has had difficulty breathing requiring bronchodilators, inhaled corticosteroids, and chest physiotherapy. He has also had diarrhea and malabsorption requiring enzyme replacement therapy. If his wife comes from a population where 1 in 10,000 people are affected by this same disorder, which of the following best represents the likelihood a child would be affected as well?
- A. 0.01%
- B. 2%
- C. 0.5%
- D. 1% (Correct Answer)
- E. 50%
Power calculations for subgroup analyses Explanation: ***Correct Option: 1%***
- The patient's symptoms (difficulty breathing requiring bronchodilators, inhaled corticosteroids, and chest physiotherapy; diarrhea and malabsorption requiring enzyme replacement therapy) are classic for **cystic fibrosis (CF)**, an **autosomal recessive disorder**.
- For an autosomal recessive disorder with a prevalence of 1 in 10,000 in the general population, **q² = 1/10,000**, so **q = 1/100 = 0.01**. The carrier frequency **(2pq)** is approximately **2q = 2 × (1/100) = 1/50 = 0.02**.
- The affected man is **homozygous recessive (aa)** and will always pass on the recessive allele. His wife has a **1/50 chance of being a carrier (Aa)**. If she is a carrier, she has a **1/2 chance of passing on the recessive allele**.
- Therefore, the probability of an affected child = **(Probability wife is a carrier) × (Probability wife passes recessive allele) = 1/50 × 1/2 = 1/100 = 1%**.
*Incorrect Option: 0.01%*
- This percentage is too low and does not correctly account for the carrier frequency in the population and the probability of transmission from a carrier mother.
*Incorrect Option: 2%*
- This represents approximately the carrier frequency (1/50 ≈ 2%), but does not account for the additional 1/2 probability that a carrier mother would pass on the recessive allele.
*Incorrect Option: 0.5%*
- This value would be correct if the carrier frequency were 1/100 instead of 1/50, which does not match the given population prevalence.
*Incorrect Option: 50%*
- **50%** would be the risk if both parents were carriers of an autosomal recessive disorder (1/4 chance = 25% for affected, but if we know one parent passes the allele, conditional probability changes). More accurately, 50% would apply if the disorder were **autosomal dominant** with one affected parent, which is not the case here.
Power calculations for subgroup analyses US Medical PG Question 4: In the study, all participants who were enrolled and randomly assigned to treatment with pulmharkimab were analyzed in the pulmharkimab group regardless of medication nonadherence or refusal of allocated treatment. A medical student reading the abstract is confused about why some participants assigned to pulmharkimab who did not adhere to the regimen were still analyzed as part of the pulmharkimab group. Which of the following best reflects the purpose of such an analysis strategy?
- A. To minimize type 2 errors
- B. To assess treatment efficacy more accurately
- C. To reduce selection bias (Correct Answer)
- D. To increase internal validity of study
- E. To increase sample size
Power calculations for subgroup analyses Explanation: ***To reduce selection bias***
- Analyzing participants in their originally assigned groups, regardless of adherence, is known as **intention-to-treat (ITT) analysis**.
- This method helps **preserve randomization** and minimizes **selection bias** that could arise if participants who did not adhere to treatment were excluded or re-assigned.
- **This is the most direct and specific purpose** of ITT analysis - preventing systematic differences between groups caused by post-randomization exclusions.
*To minimize type 2 errors*
- While ITT analysis affects statistical power, its primary purpose is not specifically to minimize **type 2 errors** (false negatives).
- ITT analysis may sometimes *increase* the likelihood of a type 2 error by diluting the treatment effect due to non-adherence.
*To assess treatment efficacy more accurately*
- ITT analysis assesses the **effectiveness** of *assigning* a treatment in a real-world setting, rather than the pure biological **efficacy** of the treatment itself.
- Efficacy is better assessed by a **per-protocol analysis**, which only includes compliant participants.
- ITT provides a more **conservative** and **pragmatic** estimate of treatment effect.
*To increase internal validity of study*
- While ITT analysis does contribute to **internal validity** by maintaining randomization, this is a **broader, secondary benefit** rather than the primary purpose.
- Internal validity encompasses many aspects of study design; ITT specifically addresses **post-randomization bias prevention**.
- The more precise answer is that ITT reduces **selection bias**, which is one specific threat to internal validity.
- Many other design features also contribute to internal validity (blinding, standardized protocols, etc.), making this option less specific.
*To increase sample size*
- ITT analysis includes all randomized participants, so it maintains the initial **sample size** that was randomized.
- However, the primary purpose is to preserve the integrity of randomization and prevent bias, not simply to increase the number of participants in the final analysis.
Power calculations for subgroup analyses US Medical PG Question 5: You are reading through a recent article that reports significant decreases in all-cause mortality for patients with malignant melanoma following treatment with a novel biological infusion. Which of the following choices refers to the probability that a study will find a statistically significant difference when one truly does exist?
- A. Type II error
- B. Type I error
- C. Confidence interval
- D. p-value
- E. Power (Correct Answer)
Power calculations for subgroup analyses Explanation: ***Power***
- **Power** is the probability that a study will correctly reject the null hypothesis when it is, in fact, false (i.e., will find a statistically significant difference when one truly exists).
- A study with high power minimizes the risk of a **Type II error** (failing to detect a real effect).
*Type II error*
- A **Type II error** (or **beta error**) occurs when a study fails to reject a false null hypothesis, meaning it concludes there is no significant difference when one actually exists.
- This is the **opposite** of what the question describes, which asks for the probability of *finding* a difference.
*Type I error*
- A **Type I error** (or **alpha error**) occurs when a study incorrectly rejects a true null hypothesis, concluding there is a significant difference when one does not actually exist.
- This relates to the **p-value** and the level of statistical significance (e.g., p < 0.05).
*Confidence interval*
- A **confidence interval** provides a range of values within which the true population parameter is likely to lie with a certain degree of confidence (e.g., 95%).
- It does not directly represent the probability of finding a statistically significant difference when one truly exists.
*p-value*
- The **p-value** is the probability of observing data as extreme as, or more extreme than, that obtained in the study, assuming the null hypothesis is true.
- It is used to determine statistical significance, but it is not the probability of detecting a true effect.
Power calculations for subgroup analyses US Medical PG Question 6: A research group wants to assess the safety and toxicity profile of a new drug. A clinical trial is conducted with 20 volunteers to estimate the maximum tolerated dose and monitor the apparent toxicity of the drug. The study design is best described as which of the following phases of a clinical trial?
- A. Phase 0
- B. Phase III
- C. Phase V
- D. Phase II
- E. Phase I (Correct Answer)
Power calculations for subgroup analyses Explanation: ***Phase I***
- **Phase I clinical trials** involve a small group of healthy volunteers (typically 20-100) to primarily assess **drug safety**, determine a safe dosage range, and identify side effects.
- The main goal is to establish the **maximum tolerated dose (MTD)** and evaluate the drug's pharmacokinetic and pharmacodynamic profiles.
*Phase 0*
- **Phase 0 trials** are exploratory studies conducted in a very small number of subjects (10-15) to gather preliminary data on a drug's **pharmacodynamics and pharmacokinetics** in humans.
- They involve microdoses, not intended to have therapeutic effects, and thus cannot determine toxicity or MTD.
*Phase III*
- **Phase III trials** are large-scale studies involving hundreds to thousands of patients to confirm the drug's **efficacy**, monitor side effects, compare it to standard treatments, and collect information that will allow the drug to be used safely.
- These trials are conducted after safety and initial efficacy have been established in earlier phases.
*Phase V*
- "Phase V" is not a standard, recognized phase in the traditional clinical trial classification (Phase 0, I, II, III, IV).
- This term might be used in some non-standard research contexts or for post-marketing studies that go beyond Phase IV surveillance, but it is not a formal phase for initial drug development.
*Phase II*
- **Phase II trials** involve several hundred patients with the condition the drug is intended to treat, focusing on **drug efficacy** and further evaluating safety.
- While safety is still monitored, the primary objective shifts to determining if the drug works for its intended purpose and at what dose.
Power calculations for subgroup analyses US Medical PG Question 7: An epidemiologist is evaluating the efficacy of Noxbinle in preventing HCC deaths at the population level. A clinical trial shows that over 5 years, the mortality rate from HCC was 25% in the control group and 15% in patients treated with Noxbinle 100 mg daily. Based on this data, how many patients need to be treated with Noxbinle 100 mg to prevent, on average, one death from HCC?
- A. 20
- B. 73
- C. 10 (Correct Answer)
- D. 50
- E. 100
Power calculations for subgroup analyses Explanation: ***10***
- The **number needed to treat (NNT)** is calculated by first finding the **absolute risk reduction (ARR)**.
- **ARR** = Risk in control group - Risk in treatment group = 25% - 15% = **10%** (or 0.10).
- **NNT = 1 / ARR** = 1 / 0.10 = **10 patients**.
- This means that **10 patients must be treated with Noxbinle to prevent one death from HCC** over 5 years.
*20*
- This would result from an ARR of 5% (1/0.05 = 20), which is not supported by the data.
- May arise from miscalculating the risk difference or incorrectly halving the actual ARR.
*73*
- This value does not correspond to any standard calculation of NNT from the given mortality rates.
- May result from confusion with other epidemiological measures or calculation error.
*50*
- This would correspond to an ARR of 2% (1/0.02 = 50), which significantly underestimates the actual risk reduction.
- Could result from incorrectly calculating the difference as a proportion rather than absolute percentage points.
*100*
- This would correspond to an ARR of 1% (1/0.01 = 100), grossly underestimating the treatment benefit.
- May result from confusing ARR with relative risk reduction or other calculation errors.
Power calculations for subgroup analyses US Medical PG Question 8: A pharmaceutical corporation is developing a research study to evaluate a novel blood test to screen for breast cancer. They enrolled 800 patients in the study, half of which have breast cancer. The remaining enrolled patients are age-matched controls who do not have the disease. Of those in the diseased arm, 330 are found positive for the test. Of the patients in the control arm, only 30 are found positive. What is this test’s sensitivity?
- A. 330 / (330 + 30)
- B. 330 / (330 + 70) (Correct Answer)
- C. 370 / (30 + 370)
- D. 370 / (70 + 370)
- E. 330 / (400 + 400)
Power calculations for subgroup analyses Explanation: ***330 / (330 + 70)***
- **Sensitivity** measures the proportion of actual **positives** that are correctly identified as such.
- In this study, there are **400 diseased patients** (half of 800). Of these, 330 tested positive (true positives), meaning 70 tested negative (false negatives). So sensitivity is **330 / (330 + 70)**.
*330 / (330 + 30)*
- This calculation represents the **positive predictive value**, which is the probability that subjects with a positive screening test truly have the disease. It uses **true positives / (true positives + false positives)**.
- It does not correctly calculate **sensitivity**, which requires knowing the total number of diseased individuals.
*370 / (30 + 370)*
- This expression is attempting to calculate **specificity**, which is the proportion of actual negatives that are correctly identified. It would be **true negatives / (true negatives + false positives)**.
- However, the numbers used are incorrect for specificity in this context given the data provided.
*370 / (70 + 370)*
- This formula is an incorrect combination of values and does not represent any standard epidemiological measure like **sensitivity** or **specificity**.
- It is attempting to combine false negatives (70) and true negatives (370 from control arm) in a non-standard way.
*330 / (400 + 400)*
- This calculation attempts to divide true positives by the total study population (800 patients).
- This metric represents the **prevalence of true positives within the entire study cohort**, not the test's **sensitivity**.
Power calculations for subgroup analyses US Medical PG Question 9: You are currently employed as a clinical researcher working on clinical trials of a new drug to be used for the treatment of Parkinson's disease. Currently, you have already determined the safe clinical dose of the drug in a healthy patient. You are in the phase of drug development where the drug is studied in patients with the target disease to determine its efficacy. Which of the following phases is this new drug currently in?
- A. Phase 4
- B. Phase 1
- C. Phase 2 (Correct Answer)
- D. Phase 0
- E. Phase 3
Power calculations for subgroup analyses Explanation: ***Phase 2***
- **Phase 2 trials** involve studying the drug in patients with the target disease to assess its **efficacy** and further evaluate safety, typically involving a few hundred patients.
- The question describes a stage after safe dosing in healthy patients (Phase 1) and before large-scale efficacy confirmation (Phase 3), focusing on efficacy in the target population.
*Phase 4*
- **Phase 4 trials** occur **after a drug has been approved** and marketed, monitoring long-term effects, optimal use, and rare side effects in a diverse patient population.
- This phase is conducted post-market approval, whereas the question describes a drug still in development prior to approval.
*Phase 1*
- **Phase 1 trials** primarily focus on determining the **safety and dosage** of a new drug in a **small group of healthy volunteers** (or sometimes patients with advanced disease if the drug is highly toxic).
- The question states that the safe clinical dose in a healthy patient has already been determined, indicating that Phase 1 has been completed.
*Phase 0*
- **Phase 0 trials** are exploratory, very early-stage studies designed to confirm that the drug reaches the target and acts as intended, typically involving a very small number of doses and participants.
- These trials are conducted much earlier in the development process, preceding the determination of safe clinical doses and large-scale efficacy studies.
*Phase 3*
- **Phase 3 trials** are large-scale studies involving hundreds to thousands of patients to confirm **efficacy**, monitor side effects, compare it to commonly used treatments, and collect information that will allow the drug to be used safely.
- While Phase 3 does assess efficacy, it follows Phase 2 and is typically conducted on a much larger scale before submitting for regulatory approval.
Power calculations for subgroup analyses US Medical PG Question 10: A health system implements a new sepsis protocol across 20 hospitals. A researcher plans to evaluate effectiveness using a stepped-wedge cluster randomized design where hospitals sequentially adopt the protocol every 3 months. She calculates sample size based on individual patient outcomes (mortality) needing 2,000 patients total. The biostatistician identifies a critical error. Evaluate what modification is needed.
- A. Adjust for multiple time periods using Bonferroni correction
- B. Use hospital-level outcomes instead of patient-level outcomes as unit of analysis
- C. Increase alpha to 0.10 to account for cluster randomization reducing power
- D. Include random effects for both hospital and time period in power calculation
- E. Account for intra-cluster correlation coefficient (ICC) requiring substantial sample size inflation (Correct Answer)
Power calculations for subgroup analyses Explanation: ***Account for intra-cluster correlation coefficient (ICC) requiring substantial sample size inflation***
- In cluster-randomized designs, observations within the same cluster (hospital) are not independent; the **Intra-cluster Correlation Coefficient (ICC)** quantifies this correlation and must be used to calculate a **design effect**.
- Neglecting the ICC leads to an **underpowered study** because the effective sample size is smaller than the total number of individual patients measured.
*Adjust for multiple time periods using Bonferroni correction*
- **Bonferroni correction** is used to control for **Type I error** when performing multiple independent hypothesis tests, not for determining sample size in nested longitudinal designs.
- While the stepped-wedge design involves multiple time points, the primary analysis typically uses a **single model** (e.g., GEE or GLMM) that accounts for time as a fixed effect.
*Use hospital-level outcomes instead of patient-level outcomes as unit of analysis*
- While the hospital is the **unit of randomization**, using hospital-level means as the unit of analysis simplifies the data and causes a significant loss of **statistical information** and precision.
- Modern biostatistical methods utilize **multilevel modeling** to maintain the richness of patient-level data while adjusting for the cluster-level randomization.
*Include random effects for both hospital and time period in power calculation*
- While random effects are important for the **analysis phase**, the "critical error" identified in the prompt refers to the initial failure to inflate the sample size based on **clustering (ICC)**.
- Power calculations for stepped-wedge designs are complex and certainly involve time parameters, but **ICC-based inflation** is the most fundamental adjustment required when moving from individual to cluster randomization.
*Increase alpha to 0.10 to account for cluster randomization reducing power*
- Increasing the **alpha level** (significance threshold) is not a standard or scientifically acceptable method to compensate for the loss of power due to **clustering**.
- Standard practice mandates maintaining an **alpha of 0.05** while appropriately increasing the **sample size** or number of clusters to reach the desired power (usually 80-90%).
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