Adaptive sample size methods US Medical PG Practice Questions and MCQs
Practice US Medical PG questions for Adaptive sample size methods. These multiple choice questions (MCQs) cover important concepts and help you prepare for your exams.
Adaptive sample size methods US Medical PG Question 1: Researchers are studying the effects of a new medication for the treatment of type 2 diabetes. A randomized group of 100 subjects is given the new medication 1st for 2 months, followed by a washout period of 2 weeks, and then administration of the gold standard medication for 2 months. Another randomized group of 100 subjects is given the gold standard medication 1st for 2 months, followed by a washout period of 2 weeks, and then administration of the new medication for 2 months. What is the main disadvantage of this study design?
- A. Hawthorne effect
- B. Increasing selection bias
- C. Increasing confounding bias
- D. Decreasing power
- E. Carryover effect (Correct Answer)
Adaptive sample size methods Explanation: ***Carryover effect***
- The primary disadvantage here is the **carryover effect**, where the effects of the first treatment (new medication or gold standard) may persist into the period when the second treatment is administered, even after a washout period.
- This can **mask or alter the true effect** of the second treatment, making it difficult to accurately assess their individual efficacy.
*Hawthorne effect*
- The **Hawthorne effect** refers to subjects improving their behavior or performance in response to being observed or studied, not specifically an issue with sequential treatment administration.
- It would affect both groups equally and doesn't explain a disadvantage inherent to the crossover design itself.
*Increasing selection bias*
- **Selection bias** occurs when the randomization process fails to create comparable groups, but this study design involves **randomization** into two groups, and then a crossover, which typically aims to *reduce* selection bias by having each participant serve as their own control.
- The sequential administration within a randomized crossover design actually helps to mitigate selection bias between treatment arms.
*Increasing confounding bias*
- **Confounding bias** occurs when an unmeasured variable is associated with both the exposure and the outcome, distorting the observed relationship.
- This crossover design, where each participant receives both treatments, is intended to *reduce* confounding by inter-individual variability, as each subject acts as their own control, rather than increasing it.
*Decreasing power*
- **Power** is the ability of a study to detect a true effect if one exists. Crossover designs often *increase* statistical power compared to parallel designs because each participant receives both treatments, reducing inter-individual variability.
- This design typically requires a smaller sample size to achieve the same power as a parallel group study, so decreased power is not a disadvantage.
Adaptive sample size methods US Medical PG Question 2: 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
Adaptive sample size methods 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.
Adaptive sample size methods US Medical PG Question 3: An investigator is measuring the blood calcium level in a sample of female cross country runners and a control group of sedentary females. If she would like to compare the means of the two groups, which statistical test should she use?
- A. Chi-square test
- B. Linear regression
- C. t-test (Correct Answer)
- D. ANOVA (Analysis of Variance)
- E. F-test
Adaptive sample size methods Explanation: ***t-test***
- A **t-test** is appropriate for comparing the means of two independent groups, such as the blood calcium levels between runners and sedentary females.
- It assesses whether the observed difference between the two sample means is statistically significant or occurred by chance.
*Chi-square test*
- The **chi-square test** is used to analyze categorical data to determine if there is a significant association between two variables.
- It is not suitable for comparing continuous variables like blood calcium levels.
*Linear regression*
- **Linear regression** is used to model the relationship between a dependent variable (outcome) and one or more independent variables (predictors).
- It aims to predict the value of a variable based on the value of another, rather than comparing means between groups.
*ANOVA (Analysis of Variance)*
- **ANOVA** is used to compare the means of **three or more independent groups**.
- Since there are only two groups being compared in this scenario, a t-test is more specific and appropriate.
*F-test*
- The **F-test** is primarily used to compare the variances of two populations or to assess the overall significance of a regression model.
- While it is the basis for ANOVA, it is not the direct test for comparing the means of two groups.
Adaptive sample size methods US Medical PG Question 4: 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)
Adaptive sample size methods 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.
Adaptive sample size methods US Medical PG Question 5: 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)
Adaptive sample size methods 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.
Adaptive sample size methods US Medical PG Question 6: A researcher is examining the relationship between socioeconomic status and IQ scores. The IQ scores of young American adults have historically been reported to be distributed normally with a mean of 100 and a standard deviation of 15. Initially, the researcher obtains a random sampling of 300 high school students from public schools nationwide and conducts IQ tests on all participants. Recently, the researcher received additional funding to enable an increase in sample size to 2,000 participants. Assuming that all other study conditions are held constant, which of the following is most likely to occur as a result of this additional funding?
- A. Increase in risk of systematic error
- B. Increase in range of the confidence interval
- C. Decrease in standard deviation
- D. Increase in probability of type II error
- E. Decrease in standard error of the mean (Correct Answer)
Adaptive sample size methods Explanation: ***Decrease in standard error of the mean***
- **Increasing the sample size** (n) leads to a **decrease in the standard error of the mean** (SEM), which is calculated as σ/√n.
- A smaller SEM indicates that our sample mean is a more **precise estimate** of the true population mean.
*Increase in risk of systematic error*
- **Systematic error** is related to flaws in study design or implementation and is not directly affected by an increase in sample size.
- A larger sample size generally helps in detecting a true effect if one exists, but does not inherently introduce or correct systematic bias.
*Increase in range of the confidence interval*
- An **increase in sample size** typically leads to a **narrower confidence interval**, not a wider one, because the standard error of the mean decreases.
- A narrower confidence interval implies greater precision in estimating the population parameter.
*Decrease in standard deviation*
- The **standard deviation** is a measure of the data's spread within a sample or population and is an intrinsic characteristic of the data itself.
- Increasing the sample size typically does not change the true standard deviation of the population; it only provides a **more accurate estimate** of it.
*Increase in probability of type II error*
- An **increase in sample size** generally leads to an **increase in statistical power**, which in turn **decreases the probability of a Type II error** (failing to reject a false null hypothesis).
- A larger sample makes it easier to detect a true difference or effect if one exists.
Adaptive sample size methods US Medical PG Question 7: You submit a paper to a prestigious journal about the effects of coffee consumption on mesothelioma risk. The first reviewer lauds your clinical and scientific acumen, but expresses concern that your study does not have adequate statistical power. Statistical power refers to which of the following?
- A. The probability of detecting an association when no association exists.
- B. The probability of not detecting an association when an association does exist.
- C. The probability of detecting an association when an association does exist. (Correct Answer)
- D. The first derivative of work.
- E. The square root of the variance.
Adaptive sample size methods Explanation: ***The probability of detecting an association when an association does exist.***
- **Statistical power** is defined as the probability that a study will correctly reject a false null hypothesis, meaning it will detect a true effect or association if one exists.
- A study with **adequate statistical power** is less likely to miss a real effect.
*The probability of detecting an association when no association exists.*
- This describes a **Type I error** or **false positive**, often represented by **alpha (α)**.
- It is the probability of incorrectly concluding an effect or association exists when, in reality, there is none.
*The probability of not detecting an association when an association does exist.*
- This refers to a **Type II error** or **false negative**, represented by **beta (β)**.
- **Statistical power** is calculated as **1 - β**, so this option describes the complement of power.
*The first derivative of work.*
- The first derivative of work with respect to time represents **power** in physics, which is the rate at which work is done.
- This option is a **distractor** from physics and is unrelated to statistical power in research.
*The square root of the variance.*
- The **square root of the variance** is the **standard deviation**, a measure of the dispersion or spread of data.
- This is a statistical concept but is not the definition of statistical power.
Adaptive sample size methods US Medical PG Question 8: A 52-year-old man presents to the office for a regular health checkup. He was diagnosed with type 2 diabetes mellitus 6 years ago and has been taking metformin alone. Over the past year, his daily blood glucose measurements have gradually been increasing. During his previous visit, his HbA1c level was 7.9% and the doctor mentioned the possibility of requiring an additional medication to keep his blood sugar under better control. Today, his HbA1c is 9%. The doctor mentions a research article that has been conducted on a randomized and controlled group of 200 subjects studying a new anti-diabetic medication. It has been shown to significantly reduce glucose levels and HbA1c levels compared to the current gold standard treatment. Possible adverse effects, however, are still being studied, though the authors believe that they will be minimal. In this study, what would most likely increase the chances of detecting a significant adverse effect?
- A. Decreasing post-market surveillance time
- B. Non-randomization
- C. Decreasing sample size
- D. Increasing selection bias
- E. Increasing sample size (Correct Answer)
Adaptive sample size methods Explanation: ***Increasing sample size***
- A **larger sample size** increases the **statistical power** of a study, making it more likely to detect a real difference or effect, including rare adverse events.
- With more participants, there's a higher chance of observing adverse effects that might only occur in a small percentage of individuals.
*Decreasing post-market surveillance time*
- **Post-market surveillance** occurs *after* a drug is approved and involves monitoring thousands, or even millions, of patients for adverse events.
- **Decreasing** this time would *reduce* the likelihood of detecting rare or long-term adverse effects, as the exposure period and number of observed patients would be smaller.
*Non-randomization*
- **Non-randomization** can introduce **confounding variables** and **bias**, making it difficult to attribute observed effects solely to the medication.
- While it might reveal an association, it doesn't necessarily strengthen the ability to precisely identify significant adverse effects versus other contributing factors.
*Decreasing sample size*
- A **smaller sample size** reduces the **statistical power** of a study, making it less likely to detect a true difference or effect, especially for uncommon adverse events.
- Rare adverse effects are less likely to be observed in a small group of participants.
*Increasing selection bias*
- **Selection bias** occurs when the study participants are not representative of the general population or when groups are not comparable, leading to skewed results.
- This bias can *obscure* or *misrepresent* the true incidence of adverse effects, making accurate detection more difficult, rather than increasing it.
Adaptive sample size methods US Medical PG Question 9: You are interested in studying the etiology of heart failure reduced ejection fraction (HFrEF) and attempt to construct an appropriate design study. Specifically, you wish to look for potential causality between dietary glucose consumption and HFrEF. Which of the following study designs would allow you to assess for and determine this causality?
- A. Cross-sectional study
- B. Case series
- C. Cohort study (Correct Answer)
- D. Case-control study
- E. Randomized controlled trial
Adaptive sample size methods Explanation: ***Cohort study***
- A **cohort study** observes a group of individuals over time to identify risk factors and outcomes, allowing for the assessment of **temporal relationships** between exposure (dietary glucose) and outcome (HFrEF).
- This design is suitable for establishing a potential **causal link** as it tracks participants from exposure to outcome, enabling the calculation of incidence rates and relative risks.
*Cross-sectional study*
- A **cross-sectional study** measures exposure and outcome simultaneously at a single point in time, making it impossible to determine the **temporal sequence** of events.
- This design can only identify **associations** or correlations, not causation, as it cannot establish whether high glucose consumption preceded HFrEF.
*Case series*
- A **case series** describes characteristics of a group of patients with a particular disease or exposure, often to highlight unusual clinical features, but it lacks a **comparison group**.
- It cannot assess causality because it does not provide information on the frequency of exposure in healthy individuals or the incidence of the disease in unexposed individuals.
*Case-control study*
- A **case-control study** compares individuals with the outcome (cases) to those without the outcome (controls) to determine past exposures, which makes it prone to **recall bias**.
- While it can suggest associations, it cannot definitively establish a temporal relationship or causation as the outcome is already known when exposure is assessed.
*Randomized controlled trial*
- A **randomized controlled trial (RCT)** is the gold standard for establishing causation by randomly assigning participants to an intervention or control group, but it may not be ethical or feasible for studying long-term dietary exposures and chronic diseases like HFrEF due to the long follow-up period and complexity of diet.
- While ideal for causality, directly controlling and randomizing dietary glucose intake over decades to observe HFrEF development might be practically challenging or unethical.
Adaptive sample size methods US Medical PG Question 10: A patient is in the ICU for diabetic ketoacidosis and is currently on an insulin drip. His electrolytes are being checked every hour and his potassium is notable for the following measures:
1. 5.1 mEq/L
2. 5.8 mEq/L
3. 6.1 mEq/L
4. 6.2 mEq/L
5. 5.9 mEq/L
6. 5.1 mEq/L
7. 4.0 mEq/L
8. 3.1 mEq/L
Which of the following is the median potassium value of this data set?
- A. 6.05
- B. 5.10
- C. 5.16
- D. 5.45 (Correct Answer)
- E. 3.10
Adaptive sample size methods Explanation: ***5.45***
- To find the **median**, first arrange the potassium values in ascending order: 3.1, 4.0, 5.1, 5.1, 5.8, 5.9, 6.1, 6.2.
- Since there are **eight** (an even number) values, the median is the average of the two middle values (the 4th and 5th values): (5.1 + 5.8) / 2 = 10.9 / 2 = **5.45**.
*6.05*
- This value might be obtained by incorrectly averaging a different pair of numbers or miscalculating the average of the sorted data set.
- It is not the correct median for this particular data set of potassium values.
*5.10*
- While 5.1 is present twice in the data set, and is one of the middle values, it is not the **median** because the **median** for an even number of values is the average of the two middle numbers, not just one of them.
- This would be the median if the values were 3.1, 4.0, 5.1, 5.1, 5.1, 5.8, 5.9, 6.1.
*5.16*
- This value does not correspond to any of the numbers in the data set nor does it result from the correct calculation of the **median**.
- It might represent an incorrect average or a miscalculation of a percentile.
*3.10*
- This value is the **minimum** potassium level recorded, not the median.
- The median represents the middle value in a sorted data set, while the minimum is the lowest value.
More Adaptive sample size methods US Medical PG questions available in the OnCourse app. Practice MCQs, flashcards, and get detailed explanations.
