RCTs — MCQs

Q: A randomized controlled trial is conducted investigating the effects of different diagnostic imaging modalities on breast cancer mortality. 8,000 women are randomized to receive either conventional mammography or conventional mammography with breast MRI. The primary outcome is survival from the time of breast cancer diagnosis. The conventional mammography group has a median survival after diagnosis of 17.0 years. The MRI plus conventional mammography group has a median survival of 19.5 years. If this difference is statistically significant, which form of bias may be affecting the results?

Lead-time bias. ***Lead-time bias*** - This bias occurs when a screening test diagnoses a disease earlier, making **survival appear longer** even if the actual time of death is unchanged. - In this scenario, adding **MRI** may detect breast cancer at an earlier, asymptomatic stage, artificially extending the apparent survival duration from diagnosis without necessarily changing the ultimate prognosis. *Recall bias* - **Recall bias** applies to retrospective studies where subjects are asked to recall past exposures, and those with the outcome are more likely to remember potential exposures. - It's irrelevant here as this is a **prospective randomized controlled trial** studying objective survival outcomes, not subjective past recollections. *Selection bias* - **Selection bias** occurs when participants are not randomly assigned to groups, leading to systematic differences between the groups influencing the outcome. - This study is a **randomized controlled trial**, which is designed to minimize selection bias by ensuring participants have an equal chance of being assigned to either treatment arm. *Misclassification bias* - **Misclassification bias** happens when either the exposure or the outcome is incorrectly categorized, leading to erroneous associations. - This study uses objective diagnostic imaging and survival data, thus reducing the likelihood of **misclassification of diagnosis or survival status**. *Because this study is a randomized controlled trial, it is free of bias* - While **randomized controlled trials (RCTs)** are considered the **gold standard** for minimizing bias, they are not entirely immune to all forms of bias. - **Lead-time bias**, for instance, can still occur in RCTs involving screening or early diagnosis, as seen in this example, and other biases like **information bias** or **reporting bias** can also arise.

Q: A group of bariatric surgeons are investigating a novel surgically-placed tube that drains a portion of the stomach following each meal. They are interested in studying its efficacy in facilitating weight loss in obese adults with BMIs > 40 kg/m2 who have failed to lose weight through non-surgical options. After randomizing 150 patients to undergoing the surgical tube procedure and 150 patients to non-surgical weight loss options (e.g., diet, exercise), the surgeons found that, on average, participants in the surgical treatment group lost 15% of their total body weight in comparison to 4% in the non-surgical group. Which of the following statistical tests is an appropriate initial test to evaluate if this difference in weight loss between the two groups is statistically significant?

Unpaired two-sample t-test. ***Unpaired two-sample t-test*** - The goal is to compare the **means of two independent groups** (surgical vs. non-surgical) on a continuous outcome (percentage of weight loss). - An unpaired t-test is ideal for determining if the **observed difference between these two group means** is statistically significant. *Kaplan-Meier analysis* - This analysis is used to estimate and compare **survival curves** or time-to-event data between groups. - It is not suitable for comparing the **mean weight loss** between two independent groups. *Paired two-sample t-test* - A paired t-test is used when comparing two measurements from the **same individuals** or **matched pairs**. - Here, the two groups are distinct and independent, not paired in any way. *Multiple linear regression* - This is used to model the **relationship between a dependent variable** and **two or more independent variables**. - While useful for predicting weight loss based on multiple factors, it's not the most direct or initial test for simply comparing the mean weight loss between two groups. *Pearson correlation coefficient* - The Pearson correlation coefficient measures the **strength and direction of a linear relationship between two continuous variables**. - It does not compare the means of two independent groups, but rather assesses the **degree to which two variables change together**.

Q: A randomized controlled trial is conducted to evaluate the relationship between the angiotensin receptor blocker losartan and cardiovascular death in patients with congestive heart failure (diagnosed as ejection fraction < 30%) who are already being treated with an angiotensin-converting enzyme (ACE) inhibitor and a beta blocker. Patients are randomized either to losartan (N = 1500) or placebo (N = 1400). The results of the study show: Cardiovascular death No cardiovascular death Losartan + ACE inhibitor + beta blocker 300 1200 Placebo + ACE inhibitor + beta blocker 350 1050 Based on this information, if 200 patients with congestive heart failure and an ejection fraction < 30% were treated with losartan in addition to an ACE inhibitor and a beta blocker, on average, how many cases of cardiovascular death would be prevented?

10. ***10*** - To calculate the number of deaths prevented, first, determine the **Absolute Risk Reduction (ARR)**. - The **Control Event Rate (CER)** = 350 deaths / 1400 total placebo patients = 0.25 (25%). The **Experimental Event Rate (EER)** = 300 deaths / 1500 total losartan patients = 0.20 (20%). - **ARR** = CER - EER = 0.25 - 0.20 = 0.05. - To find how many deaths are prevented in 200 patients, multiply the ARR by the number of patients: 0.05 * 200 = **10 deaths prevented**. *0.25* - This value represents the **Control Event Rate (CER)**, which is the proportion of deaths in the placebo group (350/1400 = 0.25). - It does not represent the number of deaths prevented. *20* - This value may result from an incorrect calculation of the **Absolute Risk Reduction (ARR)** or an error in applying it to the number of patients. - For example, if EER was incorrectly calculated or if the ARR was doubled. *50* - This value would be obtained if you incorrectly assumed a much higher ARR or incorrectly calculated the total number of deaths. - One possible miscalculation relates to using the **Number Needed to Treat (NNT)** incorrectly. *0.05* - This value represents the **Absolute Risk Reduction (ARR)**, which is the difference between the event rates in the control and experimental groups (0.25 - 0.20 = 0.05). - It is not the total number of deaths prevented but rather the per-patient reduction in risk.

Q: A randomized controlled trial was initiated to evaluate a novel DPP-4 inhibitor for blood glucose management in diabetic patients. The study used a commonly prescribed sulfonylurea as the standard of care treatment. 2,000 patients were enrolled in the study with 1,000 patients in each arm. One of the primary outcomes was the development of diabetic nephropathy during treatment. This outcome occurred in 68 patients on the DPP-4 inhibitor and 134 patients on the sulfonylurea. What is the relative risk reduction (RRR) for patients using the DPP-4 inhibitor compared with the sulfonylurea?

49%. ***49%*** - To calculate **relative risk reduction (RRR)**, first determine the **event rate (ER)** for each group. - ER (DPP-4 inhibitor) = 68/1000 = 0.068. ER (Sulfonylurea) = 134/1000 = 0.134. - Next, calculate the **absolute risk reduction (ARR)**: ARR = ER (Sulfonylurea) - ER (DPP-4 inhibitor) = 0.134 - 0.068 = 0.066. - Finally, calculate RRR: RRR = ARR / ER (Sulfonylurea) = 0.066 / 0.134 ≈ 0.4925 or **49%**. *23%* - This value is incorrect and does not result from the proper application of the **relative risk reduction (RRR)** formula. - A common mistake is to reverse the subtrahend and minuend in the numerator or denominator. *33%* - This value is incorrect and does not result from the proper application of the **relative risk reduction (RRR)** formula. - Incorrect calculations in either the numerator or denominator of the **RRR formula** would lead to this incorrect result. *59%* - This value is incorrect and is likely the result of an error in calculating either the **absolute risk reduction (ARR)** or dividing it by the wrong **event rate**. - Always ensure the correct event rates are used for the control group and the intervention group. *43%* - This value is incorrect and does not align with the correct calculation of **relative risk reduction (RRR)**. - Errors in setting up the formula or executing the division could lead to this result.

Q: A resident in the department of obstetrics and gynecology is reading about a randomized clinical trial from the late 1990s that was conducted to compare breast cancer mortality risk, disease localization, and tumor size in women who were randomized to groups receiving either annual mammograms starting at age 40 or annual mammograms starting at age 50. One of the tables in the study compares the two experimental groups with regard to socioeconomic demographics (e.g., age, income), medical conditions at the time of recruitment, and family history of breast cancer. The purpose of this table is most likely to evaluate which of the following?

Randomization. ***Randomization*** - In a randomized clinical trial, the purpose of comparing baseline characteristics between experimental groups is to assess if **randomization successfully distributed potential confounders** evenly. - An even distribution of baseline characteristics suggests that any observed differences in outcomes are more likely due to the intervention rather than **pre-existing differences** between the groups. *Observer bias* - **Observer bias** occurs when researchers' expectations influence their observations or interpretation of results, which is not evaluated by comparing baseline demographics. - This type of bias is typically mitigated through **blinding** of researchers or participants, rather than checking baseline characteristics. *Statistical power* - **Statistical power** refers to the probability of correctly rejecting a false null hypothesis and detecting a true effect. - It is determined by factors like sample size and effect size, not by the **balance of baseline characteristics** between groups. *Effect modification* - **Effect modification** occurs when the effect of an exposure on an outcome varies across different levels of a third variable. - This is an **analytical consideration** explored in later stages of data analysis, not a concern addressed by comparing baseline characteristics. *Confounding* - **Confounding** occurs when an extraneous variable is associated with both the exposure and the outcome, distorting the true relationship. - While the baseline table helps verify that potential confounders are evenly distributed, the primary purpose is to evaluate whether **randomization was successful**, not to directly assess confounding as an analysis concern.

Q: 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?

Increasing sample size. ***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.

A study seeks to investigate the therapeutic efficacy of treating asymptomatic subclinical hypothyroidism in preventing symptoms of hypothyroidism. The investigators found 300 asymptomatic patients with subclinical hypothyroidism, defined as serum thyroid-stimulating hormone (TSH) of 5 to 10 μU/mL with normal serum thyroxine (T4) levels. The patients were randomized to either thyroxine 75 μg daily or placebo. Both investigators and study subjects were blinded. Baseline patient characteristics were distributed similarly in the treatment and control group (p > 0.05). Participants' serum T4 and TSH levels and subjective quality of life were evaluated at a 3-week follow-up. No difference was found between the treatment and placebo groups. Which of the following is the most likely explanation for the results of this study?

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 randomized control double-blind study is conducted on the efficacy of 2 sulfonylureas. The study concluded that medication 1 was more efficacious in lowering fasting blood glucose than medication 2 (p ≤ 0.05; 95% CI: 14 [10-21]). Which of the following is true regarding a 95% confidence interval (CI)?

A randomized controlled trial is conducted investigating the effects of different diagnostic imaging modalities on breast cancer mortality. 8,000 women are randomized to receive either conventional mammography or conventional mammography with breast MRI. The primary outcome is survival from the time of breast cancer diagnosis. The conventional mammography group has a median survival after diagnosis of 17.0 years. The MRI plus conventional mammography group has a median survival of 19.5 years. If this difference is statistically significant, which form of bias may be affecting the results?

A group of bariatric surgeons are investigating a novel surgically-placed tube that drains a portion of the stomach following each meal. They are interested in studying its efficacy in facilitating weight loss in obese adults with BMIs > 40 kg/m2 who have failed to lose weight through non-surgical options. After randomizing 150 patients to undergoing the surgical tube procedure and 150 patients to non-surgical weight loss options (e.g., diet, exercise), the surgeons found that, on average, participants in the surgical treatment group lost 15% of their total body weight in comparison to 4% in the non-surgical group. Which of the following statistical tests is an appropriate initial test to evaluate if this difference in weight loss between the two groups is statistically significant?

A randomized controlled trial is conducted to evaluate the relationship between the angiotensin receptor blocker losartan and cardiovascular death in patients with congestive heart failure (diagnosed as ejection fraction < 30%) who are already being treated with an angiotensin-converting enzyme (ACE) inhibitor and a beta blocker. Patients are randomized either to losartan (N = 1500) or placebo (N = 1400). The results of the study show: Cardiovascular death No cardiovascular death Losartan + ACE inhibitor + beta blocker 300 1200 Placebo + ACE inhibitor + beta blocker 350 1050 Based on this information, if 200 patients with congestive heart failure and an ejection fraction < 30% were treated with losartan in addition to an ACE inhibitor and a beta blocker, on average, how many cases of cardiovascular death would be prevented?

A randomized double-blind controlled trial is conducted on the efficacy of 2 different ACE-inhibitors. The null hypothesis is that both drugs will be equivalent in their blood-pressure-lowering abilities. The study concluded, however, that Medication 1 was more efficacious in lowering blood pressure than medication 2 as determined by a p-value < 0.01 (with significance defined as p ≤ 0.05). Which of the following statements is correct?

A randomized controlled trial was initiated to evaluate a novel DPP-4 inhibitor for blood glucose management in diabetic patients. The study used a commonly prescribed sulfonylurea as the standard of care treatment. 2,000 patients were enrolled in the study with 1,000 patients in each arm. One of the primary outcomes was the development of diabetic nephropathy during treatment. This outcome occurred in 68 patients on the DPP-4 inhibitor and 134 patients on the sulfonylurea. What is the relative risk reduction (RRR) for patients using the DPP-4 inhibitor compared with the sulfonylurea?

A resident in the department of obstetrics and gynecology is reading about a randomized clinical trial from the late 1990s that was conducted to compare breast cancer mortality risk, disease localization, and tumor size in women who were randomized to groups receiving either annual mammograms starting at age 40 or annual mammograms starting at age 50. One of the tables in the study compares the two experimental groups with regard to socioeconomic demographics (e.g., age, income), medical conditions at the time of recruitment, and family history of breast cancer. The purpose of this table is most likely to evaluate which of the following?

Q10

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?

RCTs US Medical PG Practice Questions and MCQs

Question 1: A study seeks to investigate the therapeutic efficacy of treating asymptomatic subclinical hypothyroidism in preventing symptoms of hypothyroidism. The investigators found 300 asymptomatic patients with subclinical hypothyroidism, defined as serum thyroid-stimulating hormone (TSH) of 5 to 10 μU/mL with normal serum thyroxine (T4) levels. The patients were randomized to either thyroxine 75 μg daily or placebo. Both investigators and study subjects were blinded. Baseline patient characteristics were distributed similarly in the treatment and control group (p > 0.05). Participants' serum T4 and TSH levels and subjective quality of life were evaluated at a 3-week follow-up. No difference was found between the treatment and placebo groups. Which of the following is the most likely explanation for the results of this study?

A. Observer effect
B. Berkson bias
C. Latency period (Correct Answer)
D. Confounding bias
E. Lead-time bias

Explanation: ***Latency period*** - A **latency period** refers to the time between exposure to a cause (e.g., treatment) and the manifestation of its effects (e.g., symptom improvement). The study's **3-week follow-up is too short** to observe the therapeutic benefits of thyroxine in subclinical hypothyroidism. - Levothyroxine (T4) has a **half-life of approximately 7 days**, and it typically takes **6-8 weeks or longer** for steady-state levels to be achieved and for clinical symptoms to improve. The slow onset of action for thyroid hormone replacement and the gradual nature of symptom resolution mean a longer observation period (typically 3-6 months) is needed to assess efficacy in hypothyroidism. - The null results likely reflect insufficient follow-up time rather than lack of treatment effect. *Observer effect* - The **observer effect**, or Hawthorne effect, occurs when subjects change their behavior because they know they are being observed. This study used **double-blinding** (both investigators and subjects), which effectively minimizes the observer effect. - The primary issue here is the lack of observed therapeutic effect due to timing, not a change in behavior due to observation. *Berkson bias* - **Berkson bias** is a form of selection bias that arises in case-control studies conducted in hospitals, where the probability of being admitted to the hospital can be affected by both exposure and disease. - This study is a **randomized controlled trial**, not a case-control study, and the selection of participants does not illustrate this specific bias. *Confounding bias* - **Confounding bias** occurs when an extraneous variable is associated with both the exposure and the outcome, distorting the observed relationship. The study states that **baseline patient characteristics were similarly distributed (p > 0.05)**, indicating successful randomization and minimization of confounding. - While confounding is a common concern in observational studies, the RCT design and reported baseline similarities make it unlikely to be the primary explanation for the null results compared to an insufficient follow-up period. *Lead-time bias* - **Lead-time bias** is a form of detection bias where early detection of a disease through screening appears to prolong survival, even if the treatment does not change the course of the disease. - This study is evaluating the **efficacy of treatment** in asymptomatic individuals with subclinical hypothyroidism, not the effect of screening on survival, making lead-time bias irrelevant to these results.

Question 2: 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

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.

Question 3: A randomized control double-blind study is conducted on the efficacy of 2 sulfonylureas. The study concluded that medication 1 was more efficacious in lowering fasting blood glucose than medication 2 (p ≤ 0.05; 95% CI: 14 [10-21]). Which of the following is true regarding a 95% confidence interval (CI)?

A. If the same study were repeated multiple times, approximately 95% of the calculated confidence intervals would contain the true population parameter. (Correct Answer)
B. The 95% confidence interval is the probability chosen by the researcher to be the threshold of statistical significance.
C. When a 95% CI for the estimated difference between groups contains the value ‘0’, the results are significant.
D. It represents the probability that chance would not produce the difference shown, 95% of the time.
E. The study is adequately powered at the 95% confidence interval.

Explanation: ***If the same study were repeated multiple times, approximately 95% of the calculated confidence intervals would contain the true population parameter.*** - This statement accurately defines the **frequentist interpretation** of a confidence interval (CI). It reflects the long-run behavior of the CI over hypothetical repetitions of the study. - A 95% CI means that if you were to repeat the experiment many times, 95% of the CIs calculated from those experiments would capture the **true underlying population parameter**. *The 95% confidence interval is the probability chosen by the researcher to be the threshold of statistical significance.* - The **alpha level (α)**, typically set at 0.05 (or 5%), is the threshold for statistical significance (p ≤ 0.05), representing the probability of a Type I error. - The 95% confidence level (1-α) is related to statistical significance, but it is not the *threshold* itself; rather, it indicates the **reliability** of the interval estimate. *When a 95% CI for the estimated difference between groups contains the value ‘0’, the results are significant.* - If a 95% CI for the difference between groups **contains 0**, it implies that there is **no statistically significant difference** between the groups at the 0.05 alpha level. - A statistically significant difference (p ≤ 0.05) would be indicated if the 95% CI **does NOT contain 0**, suggesting that the intervention had a real effect. *It represents the probability that chance would not produce the difference shown, 95% of the time.* - This statement misinterprets the meaning of a CI and probability. The chance of not producing the observed difference is typically addressed by the **p-value**, not directly by the CI in this manner. - A CI provides a **range of plausible values** for the population parameter, not a probability about the role of chance in producing the observed difference. *The study is adequately powered at the 95% confidence interval.* - **Statistical power** is the probability of correctly rejecting a false null hypothesis, typically set at 80% or 90%. It is primarily determined by sample size, effect size, and alpha level. - A 95% CI is a measure of the **precision** of an estimate, while power refers to the **ability of a study to detect an effect** if one exists. They are related but distinct concepts.

Question 4: A randomized controlled trial is conducted investigating the effects of different diagnostic imaging modalities on breast cancer mortality. 8,000 women are randomized to receive either conventional mammography or conventional mammography with breast MRI. The primary outcome is survival from the time of breast cancer diagnosis. The conventional mammography group has a median survival after diagnosis of 17.0 years. The MRI plus conventional mammography group has a median survival of 19.5 years. If this difference is statistically significant, which form of bias may be affecting the results?

A. Recall bias
B. Selection bias
C. Misclassification bias
D. Because this study is a randomized controlled trial, it is free of bias
E. Lead-time bias (Correct Answer)

Explanation: ***Lead-time bias*** - This bias occurs when a screening test diagnoses a disease earlier, making **survival appear longer** even if the actual time of death is unchanged. - In this scenario, adding **MRI** may detect breast cancer at an earlier, asymptomatic stage, artificially extending the apparent survival duration from diagnosis without necessarily changing the ultimate prognosis. *Recall bias* - **Recall bias** applies to retrospective studies where subjects are asked to recall past exposures, and those with the outcome are more likely to remember potential exposures. - It's irrelevant here as this is a **prospective randomized controlled trial** studying objective survival outcomes, not subjective past recollections. *Selection bias* - **Selection bias** occurs when participants are not randomly assigned to groups, leading to systematic differences between the groups influencing the outcome. - This study is a **randomized controlled trial**, which is designed to minimize selection bias by ensuring participants have an equal chance of being assigned to either treatment arm. *Misclassification bias* - **Misclassification bias** happens when either the exposure or the outcome is incorrectly categorized, leading to erroneous associations. - This study uses objective diagnostic imaging and survival data, thus reducing the likelihood of **misclassification of diagnosis or survival status**. *Because this study is a randomized controlled trial, it is free of bias* - While **randomized controlled trials (RCTs)** are considered the **gold standard** for minimizing bias, they are not entirely immune to all forms of bias. - **Lead-time bias**, for instance, can still occur in RCTs involving screening or early diagnosis, as seen in this example, and other biases like **information bias** or **reporting bias** can also arise.

Question 5: A group of bariatric surgeons are investigating a novel surgically-placed tube that drains a portion of the stomach following each meal. They are interested in studying its efficacy in facilitating weight loss in obese adults with BMIs > 40 kg/m2 who have failed to lose weight through non-surgical options. After randomizing 150 patients to undergoing the surgical tube procedure and 150 patients to non-surgical weight loss options (e.g., diet, exercise), the surgeons found that, on average, participants in the surgical treatment group lost 15% of their total body weight in comparison to 4% in the non-surgical group. Which of the following statistical tests is an appropriate initial test to evaluate if this difference in weight loss between the two groups is statistically significant?

A. Kaplan-Meier analysis
B. Paired two-sample t-test
C. Multiple linear regression
D. Pearson correlation coefficient
E. Unpaired two-sample t-test (Correct Answer)

Explanation: ***Unpaired two-sample t-test*** - The goal is to compare the **means of two independent groups** (surgical vs. non-surgical) on a continuous outcome (percentage of weight loss). - An unpaired t-test is ideal for determining if the **observed difference between these two group means** is statistically significant. *Kaplan-Meier analysis* - This analysis is used to estimate and compare **survival curves** or time-to-event data between groups. - It is not suitable for comparing the **mean weight loss** between two independent groups. *Paired two-sample t-test* - A paired t-test is used when comparing two measurements from the **same individuals** or **matched pairs**. - Here, the two groups are distinct and independent, not paired in any way. *Multiple linear regression* - This is used to model the **relationship between a dependent variable** and **two or more independent variables**. - While useful for predicting weight loss based on multiple factors, it's not the most direct or initial test for simply comparing the mean weight loss between two groups. *Pearson correlation coefficient* - The Pearson correlation coefficient measures the **strength and direction of a linear relationship between two continuous variables**. - It does not compare the means of two independent groups, but rather assesses the **degree to which two variables change together**.

Question 6: A randomized controlled trial is conducted to evaluate the relationship between the angiotensin receptor blocker losartan and cardiovascular death in patients with congestive heart failure (diagnosed as ejection fraction < 30%) who are already being treated with an angiotensin-converting enzyme (ACE) inhibitor and a beta blocker. Patients are randomized either to losartan (N = 1500) or placebo (N = 1400). The results of the study show: Cardiovascular death No cardiovascular death Losartan + ACE inhibitor + beta blocker 300 1200 Placebo + ACE inhibitor + beta blocker 350 1050 Based on this information, if 200 patients with congestive heart failure and an ejection fraction < 30% were treated with losartan in addition to an ACE inhibitor and a beta blocker, on average, how many cases of cardiovascular death would be prevented?

A. 0.25
B. 10 (Correct Answer)
C. 20
D. 50
E. 0.05

Explanation: ***10*** - To calculate the number of deaths prevented, first, determine the **Absolute Risk Reduction (ARR)**. - The **Control Event Rate (CER)** = 350 deaths / 1400 total placebo patients = 0.25 (25%). The **Experimental Event Rate (EER)** = 300 deaths / 1500 total losartan patients = 0.20 (20%). - **ARR** = CER - EER = 0.25 - 0.20 = 0.05. - To find how many deaths are prevented in 200 patients, multiply the ARR by the number of patients: 0.05 * 200 = **10 deaths prevented**. *0.25* - This value represents the **Control Event Rate (CER)**, which is the proportion of deaths in the placebo group (350/1400 = 0.25). - It does not represent the number of deaths prevented. *20* - This value may result from an incorrect calculation of the **Absolute Risk Reduction (ARR)** or an error in applying it to the number of patients. - For example, if EER was incorrectly calculated or if the ARR was doubled. *50* - This value would be obtained if you incorrectly assumed a much higher ARR or incorrectly calculated the total number of deaths. - One possible miscalculation relates to using the **Number Needed to Treat (NNT)** incorrectly. *0.05* - This value represents the **Absolute Risk Reduction (ARR)**, which is the difference between the event rates in the control and experimental groups (0.25 - 0.20 = 0.05). - It is not the total number of deaths prevented but rather the per-patient reduction in risk.

Question 7: A randomized double-blind controlled trial is conducted on the efficacy of 2 different ACE-inhibitors. The null hypothesis is that both drugs will be equivalent in their blood-pressure-lowering abilities. The study concluded, however, that Medication 1 was more efficacious in lowering blood pressure than medication 2 as determined by a p-value < 0.01 (with significance defined as p ≤ 0.05). Which of the following statements is correct?

A. We can accept the null hypothesis.
B. We can reject the null hypothesis. (Correct Answer)
C. This trial did not reach statistical significance.
D. There is a 0.1% chance that medication 2 is superior.
E. There is a 10% chance that medication 1 is superior.

Explanation: ***We can reject the null hypothesis.*** - A **p-value < 0.01** indicates that the observed difference is **statistically significant** at the **α = 0.05 level**, meaning there is strong evidence against the null hypothesis. - When a result is statistically significant (p < α), we **reject the null hypothesis**. This is the standard statistical terminology for concluding that the observed effect is unlikely to be due to chance alone. *We can accept the null hypothesis.* - A **p-value < 0.01** is **less than the significance level of 0.05**, providing strong evidence to **reject the null hypothesis**, not accept it. - Accepting the null hypothesis would imply there's no treatment effect, which contradicts the study's finding that Medication 1 was more efficacious. - Note: In hypothesis testing, we never truly "accept" the null hypothesis; we either reject it or fail to reject it. *This trial did not reach statistical significance.* - The trial **did reach statistical significance** because the **p-value (p < 0.01) is less than the defined significance level (p ≤ 0.05)**. - A p-value of 0.01 indicates a 1% chance that the observed results occurred by random chance if the null hypothesis were true. *There is a 0.1% chance that medication 2 is superior.* - The p-value of **p < 0.01** relates to the probability of observing the data (or more extreme data) given the null hypothesis is true, not the probability of one medication being superior. - It does not directly provide the probability of Medication 2 being superior; rather, it indicates the **unlikelihood of the observed difference** if no true difference exists. *There is a 10% chance that medication 1 is superior.* - A **p-value of < 0.01** means there is **less than a 1% chance** of observing such a result if the null hypothesis (no difference) were true, not a 10% chance of superiority. - The p-value represents the probability of observing the data, or more extreme data, assuming the **null hypothesis is true**, not the probability that one treatment is superior.

Question 8: A randomized controlled trial was initiated to evaluate a novel DPP-4 inhibitor for blood glucose management in diabetic patients. The study used a commonly prescribed sulfonylurea as the standard of care treatment. 2,000 patients were enrolled in the study with 1,000 patients in each arm. One of the primary outcomes was the development of diabetic nephropathy during treatment. This outcome occurred in 68 patients on the DPP-4 inhibitor and 134 patients on the sulfonylurea. What is the relative risk reduction (RRR) for patients using the DPP-4 inhibitor compared with the sulfonylurea?

A. 23%
B. 49% (Correct Answer)
C. 33%
D. 59%
E. 43%

Explanation: ***49%*** - To calculate **relative risk reduction (RRR)**, first determine the **event rate (ER)** for each group. - ER (DPP-4 inhibitor) = 68/1000 = 0.068. ER (Sulfonylurea) = 134/1000 = 0.134. - Next, calculate the **absolute risk reduction (ARR)**: ARR = ER (Sulfonylurea) - ER (DPP-4 inhibitor) = 0.134 - 0.068 = 0.066. - Finally, calculate RRR: RRR = ARR / ER (Sulfonylurea) = 0.066 / 0.134 ≈ 0.4925 or **49%**. *23%* - This value is incorrect and does not result from the proper application of the **relative risk reduction (RRR)** formula. - A common mistake is to reverse the subtrahend and minuend in the numerator or denominator. *33%* - This value is incorrect and does not result from the proper application of the **relative risk reduction (RRR)** formula. - Incorrect calculations in either the numerator or denominator of the **RRR formula** would lead to this incorrect result. *59%* - This value is incorrect and is likely the result of an error in calculating either the **absolute risk reduction (ARR)** or dividing it by the wrong **event rate**. - Always ensure the correct event rates are used for the control group and the intervention group. *43%* - This value is incorrect and does not align with the correct calculation of **relative risk reduction (RRR)**. - Errors in setting up the formula or executing the division could lead to this result.

Question 9: A resident in the department of obstetrics and gynecology is reading about a randomized clinical trial from the late 1990s that was conducted to compare breast cancer mortality risk, disease localization, and tumor size in women who were randomized to groups receiving either annual mammograms starting at age 40 or annual mammograms starting at age 50. One of the tables in the study compares the two experimental groups with regard to socioeconomic demographics (e.g., age, income), medical conditions at the time of recruitment, and family history of breast cancer. The purpose of this table is most likely to evaluate which of the following?

A. Observer bias
B. Statistical power
C. Confounding
D. Randomization (Correct Answer)
E. Effect modification

Explanation: ***Randomization*** - In a randomized clinical trial, the purpose of comparing baseline characteristics between experimental groups is to assess if **randomization successfully distributed potential confounders** evenly. - An even distribution of baseline characteristics suggests that any observed differences in outcomes are more likely due to the intervention rather than **pre-existing differences** between the groups. *Observer bias* - **Observer bias** occurs when researchers' expectations influence their observations or interpretation of results, which is not evaluated by comparing baseline demographics. - This type of bias is typically mitigated through **blinding** of researchers or participants, rather than checking baseline characteristics. *Statistical power* - **Statistical power** refers to the probability of correctly rejecting a false null hypothesis and detecting a true effect. - It is determined by factors like sample size and effect size, not by the **balance of baseline characteristics** between groups. *Effect modification* - **Effect modification** occurs when the effect of an exposure on an outcome varies across different levels of a third variable. - This is an **analytical consideration** explored in later stages of data analysis, not a concern addressed by comparing baseline characteristics. *Confounding* - **Confounding** occurs when an extraneous variable is associated with both the exposure and the outcome, distorting the true relationship. - While the baseline table helps verify that potential confounders are evenly distributed, the primary purpose is to evaluate whether **randomization was successful**, not to directly assess confounding as an analysis concern.

Question 10: 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)

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.

Question 11: A research consortium is studying a new vaccine for respiratory syncytial virus (RSV) in premature infants compared to the current standard of care. 1000 infants were randomized to either the new vaccine group or the standard of care group. In total, 520 receive the new vaccine and 480 receive the standard of care. Of those who receive the new vaccine, 13 contract RSV. Of those who received the standard of care, 30 contract RSV. Which of the following is the absolute risk reduction of this new vaccine?

A. 4.3%
B. 3.75% (Correct Answer)
C. 6.25%
D. 1.7%
E. 2.5%

Explanation: ***3.75%*** - **Absolute Risk Reduction (ARR)** is calculated as the difference between the event rate in the control group (CER) and the event rate in the experimental group (EER). - Here, the event rate in the standard of care (control) group is (30/480) * 100% = 6.25%, and in the new vaccine (experimental) group is (13/520) * 100% = 2.5%. Therefore, ARR = 6.25% - 2.5% = **3.75%**. *4.3%* - This value might be obtained from an incorrect calculation or misinterpreting the numbers for the **risk reduction**. - It does not represent the direct difference in risk between the two groups. *6.25%* - This value represents the event rate in the **standard of care (control) group** (30/480). - It is the control event rate (CER), not the absolute risk reduction. *1.7%* - This value is not derived from the correct formula for **absolute risk reduction**. - It may arise from an incomplete or incorrect calculation of the risk difference. *2.5%* - This value represents the event rate in the **new vaccine (experimental) group** (13/520). - This is the experimental event rate (EER), not the absolute risk reduction.

Question 12: BACKGROUND: Aldosterone blockade reduces mortality and morbidity among patients with severe heart failure. We conducted a double-blind, placebo-controlled study evaluating the effect of eplerenone, a selective aldosterone blocker, on morbidity and mortality among patients with acute myocardial infarction complicated by left ventricular dysfunction and heart failure. METHODS: Patients were randomly assigned to eplerenone (25 mg per day initially, titrated to a maximum of 50 mg per day; 3,319 patients) or placebo (3,313 patients) in addition to optimal medical therapy. The study continued until 1,012 deaths occurred. The primary endpoints were death from any cause, death from cardiovascular causes, hospitalization for a heart failure exacerbation, acute myocardial infarction, stroke, or ventricular arrhythmia. RESULTS: During a mean follow-up of 16 months, there were 478 deaths in the eplerenone group and 554 deaths in the placebo group (relative risk, 0.85; 95% confidence interval, 0.75 to 0.96; p=0.008). Of these deaths, 407 in the eplerenone group and 483 in the placebo group were attributed to cardiovascular causes (relative risk, 0.83; 95% confidence interval, 0.72 to 0.94; p=0.005). The rate of the other primary endpoints, death from cardiovascular causes or hospitalization for cardiovascular events, was reduced by eplerenone (relative risk, 0.87; 95% confidence interval, 0.79 to 0.95; p=0.002), as was the secondary endpoint of death from any cause or any hospitalization (relative risk, 0.92; 95% confidence interval, 0.86 to 0.98; p=0.02). There was also a reduction in the rate of sudden death from cardiac causes (relative risk, 0.79; 95% confidence interval, 0.64 to 0.97; p=0.03). The rate of serious hyperkalemia was 5.5% in the eplerenone group and 3.9% in the placebo group (p=0.002), whereas the rate of hypokalemia was 8.4% in the eplerenone group and 13.1% in the placebo group (p<0.001). Which of the following represents the relative risk reduction (RRR) in all-cause mortality, the primary endpoint, in patients supplemented with eplerenone?

A. 0.21
B. 0.15 (Correct Answer)
C. 0.13
D. 0.17
E. 0.08

Explanation: ***0.15*** - The relative risk (RR) for **death from any cause** in the eplerenone group vs. placebo was given as 0.85. The relative risk reduction (RRR) is calculated as **1 - RR**. - Therefore, the RRR is 1 - 0.85 = **0.15**. *0.21* - This value represents the RRR for **sudden death from cardiac causes** (RR = 0.79; 1 - 0.79 = 0.21). - It does not correspond to the **all-cause mortality** endpoint, which had an RR of 0.85. *0.13* - This value is not derived from the **relative risk of 0.85** for all-cause mortality mentioned in the study. - It does not represent the correct **relative risk reduction** for this specific endpoint. *0.17* - This value is inconsistent with the **relative risk of 0.85** reported for all-cause mortality. - It does not represent the correct **relative risk reduction** for this specific endpoint. *0.08* - This value does not correspond to any RRR calculation from the relative risks provided in the study. - The correct RRR for **all-cause mortality** is 0.15, not 0.08.

Question 13: In a randomized controlled trial studying a new treatment, the primary endpoint (mortality) occurred in 14.4% of the treatment group and 16.7% of the control group. Which of the following represents the number of patients needed to treat to save one life, based on the primary endpoint?

A. 1/(0.144 - 0.167)
B. 1/(0.167 - 0.144) (Correct Answer)
C. 1/(0.300 - 0.267)
D. 1/(0.267 - 0.300)
E. 1/(0.136 - 0.118)

Explanation: ***1/(0.167 - 0.144)*** - The **Number Needed to Treat (NNT)** is calculated as **1 / Absolute Risk Reduction (ARR)**. - The **Absolute Risk Reduction (ARR)** is the difference between the event rate in the control group (16.7%) and the event rate in the treatment group (14.4%), which is **0.167 - 0.144**. *1/(0.144 - 0.167)* - This calculation represents 1 divided by the **Absolute Risk Increase**, which would be relevant if the treatment increased mortality. - The **NNT should always be a positive value**, indicating the number of patients to treat to prevent one adverse event. *1/(0.300 - 0.267)* - This option uses arbitrary numbers (0.300 and 0.267) that do not correspond to the given **mortality rates** in the problem. - It does not reflect the correct calculation for **absolute risk reduction** based on the provided data. *1/(0.267 - 0.300)* - This option also uses arbitrary numbers not derived from the problem's data, and it would result in a **negative value** for the denominator. - The difference between event rates of 0.267 and 0.300 is not present in the given information for this study. *1/(0.136 - 0.118)* - This calculation uses arbitrary numbers (0.136 and 0.118) that are not consistent with the reported **mortality rates** of 14.4% and 16.7%. - These values do not represent the **Absolute Risk Reduction** required for calculating NNT in this specific scenario.

Question 14: A researcher is investigating the risk of symptomatic intracerebral hemorrhage associated with tissue plasminogen activator (tPA) treatment in severe ischemic stroke. The outcomes of a large randomized controlled trial of ischemic stroke patients, some of whom were randomized to tPA, is shown: Symptomatic intracerebral hemorrhage No symptomatic intracerebral hemorrhage Received tPA 12 188 Did not receive tPA 25 475 Based on this data, how many patients with severe ischemic stroke would need to be treated with tPA, on average, to contribute to one case of symptomatic intracerebral hemorrhage?

A. 13
B. 6
C. 0.01
D. 1.2
E. 100 (Correct Answer)

Explanation: ***100*** - To calculate the number needed to harm (NNH), first determine the **absolute risk reduction/increase (ARR/ARI)** for symptomatic intracerebral hemorrhage. - The **risk in the tPA group** is 12 (hemorrhages) / (12 + 188) (total tPA patients) = 12/200 = 0.06. The **risk in the control group** is 25 (hemorrhages) / (25 + 475) (total control patients) = 25/500 = 0.05. - The **ARI = Risk in tPA group - Risk in control group = 0.06 - 0.05 = 0.01**. - The NNH is the reciprocal of the ARI: **NNH = 1 / ARI = 1 / 0.01 = 100**. This means 100 patients need to be treated for one additional case of symptomatic intracerebral hemorrhage due to tPA. *13* - This value does not represent the correct calculation for the **Number Needed to Harm (NNH)**. - It likely results from an incorrect application of the data or a misinterpretation of the NNH formula. *6* - This number is incorrect and does not reflect the **NNH** based on the provided data. - It might represent a calculation based on a different metric or a miscalculation of the **absolute risk increase**. *0.01* - This value represents the **absolute risk increase (ARI)** (0.06 - 0.05 = 0.01) of symptomatic intracerebral hemorrhage with tPA, not the **Number Needed to Harm (NNH)**. - The NNH is the reciprocal of the ARI, which would be 1/0.01 = 100. *1.2* - This value is not derived from the standard calculation of **Number Needed to Harm (NNH)**. - It may be the result of a miscalculation or an attempt to compare the relative risks, rather than addressing the question of treatment impact per case.

Question 15: An investigator is studying the efficacy of a new bisphosphonate analog in preventing hip fractures in patients above 60 years of age with risk factors for osteoporosis but no confirmed diagnosis. Participating patients were randomized to either pharmacologic therapy with the new bisphosphonate analog or a placebo. The results show: Hip fracture No hip fracture Pharmacologic therapy 3 97 No pharmacologic therapy 10 190 Based on this information, which of the following best represents the proportionate reduction in the risk of hip fractures brought about due to pharmacologic therapy, in comparison to the control group?

A. 5%
B. 40% (Correct Answer)
C. 3%
D. 2%
E. 60%

Explanation: ***Correct: 40%*** - The **Relative Risk Reduction (RRR)** represents the proportionate reduction in risk due to the intervention compared to the control group. - Risk in control group = 10 / (10 + 190) = 10 / 200 = **0.05** or 5% - Risk in treatment group = 3 / (3 + 97) = 3 / 100 = **0.03** or 3% - **Absolute Risk Reduction (ARR)** = 0.05 - 0.03 = 0.02 or 2% - **RRR** = ARR / (Risk in control group) = 0.02 / 0.05 = **0.40** or **40%** *Incorrect: 5%* - This value represents the **absolute risk of hip fracture** in the control group (10 hip fractures out of 200 participants). - It does not reflect the **proportionate reduction** in risk due to the intervention. *Incorrect: 3%* - This value represents the **absolute risk of hip fracture** in the pharmacologic therapy group (3 hip fractures out of 100 participants). - It does not represent the **relative reduction** in risk. *Incorrect: 2%* - This value represents the **Absolute Risk Reduction (ARR)**, which is the difference between the risk in the control group (5%) and the risk in the treatment group (3%). - Although it's part of the RRR calculation, it is not the **proportionate (relative) reduction** itself. *Incorrect: 60%* - This value is obtained by incorrectly calculating the ratio of the reduction in risk to the risk in the treatment group (0.02 / 0.03), or making other computational errors. - It does not represent the **proportionate reduction** in risk when compared to the baseline risk in the control group.

Question 16: A new formula for an anti-wrinkle cream is being tested for efficacy in a group of 362 healthy 40- to 60-year-old female volunteers. The marketing team randomizes the volunteers. Half receive the new formula and the other half of the volunteers receive the original formula. The mean age in the test group is 48 (95% CI 42-56), and the average age of the control group is 49 (95% CI 42-55). The volunteers are unaware of which formula they receive. The research and development team then compares before and after photographs of the volunteers following 6 weeks of at home application twice daily. For simplicity, the marketing team labels the photographs with "new formula" or "original formula." 98% of volunteers in the test group complete the study, and 97% of volunteers in the control group complete the study. The researchers conclude that there is improved wrinkle reduction with 6 weeks of use of the new formula. Which of the following potential biases most likely impacted this conclusion?

A. Observer bias (Correct Answer)
B. Procedure bias
C. Hawthorne effect
D. Recall bias
E. Selection bias

Explanation: ***Observer bias*** - The research and development team, who evaluated the **photographs**, were aware of whether the participants received the "new formula" or "original formula." - This knowledge could unconsciously influence their interpretation of the photos, leading them to perceive more improvement in the "new formula" group even if the change was subtle or non-existent. *Procedure bias* - This occurs when different experimental procedures are applied to different groups in a study. - In this scenario, both groups followed the same procedure of applying their assigned cream twice daily for 6 weeks, which minimizes this bias. *Hawthorne effect* - The Hawthorne effect describes a phenomenon where participants improve their performance or behavior in response to being observed. - While participants knew they were in a study, the primary issue described is with the **evaluators' knowledge**, not the participants' changed behavior due to observation. *Recall bias* - Recall bias is a type of information bias where participants inaccurately recall past exposures or events due to their current status. - This study uses before-and-after photographs for objective assessment, making participant recall of past wrinkle status less relevant. *Selection bias* - Selection bias occurs when the randomization process fails to create comparable groups, leading to systematic differences between them at baseline. - The problem states that the volunteers were randomized, and the mean ages and their confidence intervals were very similar between the groups, suggesting successful randomization and minimizing selection bias.

Question 17: In 2006, three researchers from North Carolina wanted to examine the benefits of treating the risk of suicidality in children and adolescents by looking at randomized, multicenter, controlled trials of sertraline usage compared to placebo. Their analysis found clinically significant benefits of the drug and a positive benefit-to-risk ratio for sertraline in adolescents with major depressive disorder. They also found that 64 depressed children and adolescents need to receive the drug for 1 extra patient to experience suicidality as an adverse outcome. In other words, if 64 treated individuals received sertraline, some would experience suicidality due to their illness, some would not experience suicidality, and 1 individual would become suicidal due to the unique contribution of sertraline. Which of the following statements is true for this measure (defined as the inverse of the attributable risk), which aims to describe adverse outcomes this way?

A. Higher measures indicate greater risk.
B. Input values must be probabilities of the events of interest. (Correct Answer)
C. Multiple risks can be contained and described within one result.
D. The final metric represents proportions in percentage terms.
E. The measure can include multiple events at one time.

Explanation: ***Input values must be probabilities of the events of interest.*** - The measure described (- the inverse of the **attributable risk** - or more accurately, the **Number Needed to Harm** or **NNH**) is derived from **absolute risk reduction**, which requires the risk of an event in the exposed group and the risk of the event in the unexposed/control group to be expressed as **probabilities or proportions**. - These probabilities are essential for calculating the difference in event rates, which is then inverted to get the NNH. *Higher measures indicate greater risk.* - A **higher NNH** (e.g., 64 in this case) indicates that a larger number of patients need to be treated for one additional adverse event to occur, implying a **lower risk** associated with the treatment. - Conversely, a **lower NNH** (e.g., 10) would mean fewer patients need to be treated for one additional adverse event, indicating a **higher risk**. *Multiple risks can be contained and described within one result.* - The NNH (or Number Needed to Treat) is typically calculated for a **single specific outcome** (either beneficial or harmful). - While an overall benefit-to-risk analysis might involve considering multiple outcomes, the NNH itself quantifies the impact for **one defined event**. *The final metric represents proportions in percentage terms.* - The final metric (NNH) is expressed as a **whole number** (e.g., 64), representing the number of patients. - It does **not represent a proportion or a percentage**; rather, it indicates how many individuals need to be exposed to experience one additional event. *The measure can include multiple events at one time.* - The NNH is event-specific; it calculates the number of patients for **one particular adverse event**. - To analyze multiple events, one would need to calculate **separate NNH values** for each individual event.

Question 18: A randomized, controlled trial was undertaken by a team of clinical researchers to evaluate a new drug for the treatment of cluster headaches. This type of headache (that mostly affects men) is characterized by excruciating pain on 1 side of the head. After careful randomization and controlling for all of the known confounders, a total of 200 patients with cluster headaches were divided into 2 groups. The first group of study participants received 40 mg of the new drug, X, in the form of a powder mixed with water. The second group received 80 mg of verapamil (a calcium channel blocker that is commonly prescribed for cluster headaches) in the form of a labeled pill. Participants from both groups were mixed together in rooms designated for drug research purposes and could communicate freely. After the study period has finished without any loss to follow-up or skipped treatments, the outcome (pain alleviation) was assessed by trained researchers that were blinded to treatment assignment. Study results have shown that the new drug is more efficacious than current gold standard by both clinically and statistically significant margin. Therefore, the investigators concluded that this drug should be introduced for the treatment of cluster headaches. However, their conclusions are likely to be criticized on the grounds of which of the following?

A. Intention to treat bias
B. Convenience sampling bias
C. Attrition bias
D. Observer bias
E. Response bias (Correct Answer)

Explanation: ***Response bias*** - **Response bias** is likely to occur because patients in the study were able to communicate freely and were not blinded to their treatment. Knowing whether they received the experimental drug or the control could influence their self-reported pain alleviation. - The different forms of administration (powder vs. labeled pill) and dosages (40 mg vs. 80 mg) also make it difficult to blind participants effectively, contributing to the potential for response bias. *Intention to treat bias* - **Intention-to-treat bias** occurs when participants are analyzed according to the treatment they *actually received* rather than the treatment to which they were *originally assigned*. - The study explicitly states there was "no loss to follow-up or skipped treatments," indicating that an intention-to-treat analysis (analyzing all participants in their assigned groups) would likely have been performed, thus reducing this type of bias. *Convenience sampling bias* - **Convenience sampling bias** arises when participants are selected based on their easy accessibility rather than through a random or representative process. - The question states the trial involved "careful randomization," which means participants were assigned to groups randomly, not conveniently, making this bias unlikely. *Attrition bias* - **Attrition bias** (or loss to follow-up bias) occurs when there is a differential loss of participants from study groups, impacting the representativeness of the remaining cohorts. - The study explicitly states there was "no loss to follow-up or skipped treatments," directly ruling out attrition bias. *Observer bias* - **Observer bias** (or assessor bias) occurs when researchers who assess the outcome are aware of the treatment assignments and their expectations influence the assessment. - The study explicitly states that "outcome (pain alleviation) was assessed by trained researchers that were blinded to treatment assignment," thus mitigating observer bias.

Question 19: The VALIANT trial compared the effect of captopril and valsartan on mortality in patients with myocardial infarction complicated by heart failure. Subjects were randomly assigned to treatment with either captopril or valsartan and subsequently followed for 2 years. The primary endpoint was death from any cause. The study concluded that valsartan was as effective as captopril in patients who are at high risk for cardiac events after an MI. Which of the following describes this type of study?

A. Cross-sectional study
B. Randomized controlled trial (Correct Answer)
C. Case-control study
D. Crossover study
E. Cohort study

Explanation: ***Randomized controlled trial*** - Subjects were **randomly assigned** to different treatment groups (captopril or valsartan) and followed over time to compare outcomes, which is the hallmark of a randomized controlled trial. - This design allows for the most robust comparison of treatment effects by minimizing confounding variables through randomization. *Cross-sectional study* - This type of study assesses **exposure and outcome simultaneously** at a single point in time, which does not fit the description of a follow-up over two years. - It provides a snapshot of prevalence but cannot establish causality or track changes over time. *Case-control study* - This study design **compares subjects with a specific outcome (cases)** to subjects without the outcome (controls) to look back retrospectively for differential exposures. - It does not involve random assignment to interventions or prospective follow-up. *Crossover study* - In a crossover study, each participant receives **all interventions in a sequence**, often with a washout period between treatments. - The VALIANT trial described a comparison of two distinct groups, not a sequence of treatments within the same individuals. *Cohort study* - A cohort study **follows a group of individuals over time** to observe the incidence of disease or outcomes based on exposure status. - While there is follow-up, the key differentiating factor from an RCT is the **lack of randomization to an intervention**, as exposures are typically observed rather than assigned.

Question 20: A pharmaceutical company is studying the effect of a novel compound that they have discovered to treat osteoporosis. They perform a randomized controlled clinical trial to study if this compound has an effect on the incidence of hip fractures among osteoporotic patients. They find that there is no statistical difference between the experimental and control groups so they do not pursue the compound further. Two years later, a second team tests the same compound and finds that the compound is effective, and follow up studies confirm that the compound has a statistically significant effect on fracture risk. Which of the following most likely describes what occurred in the first study?

A. Design bias
B. Type II error (Correct Answer)
C. Type I error
D. Selection bias
E. Type III error

Explanation: ***Type II error*** - A **Type II error** (or **beta error**) occurs when a study fails to detect a true effect; in this case, the first study concluded there was no difference, but a real effect of the compound existed. - This typically happens when the study has **insufficient statistical power**, often due to a small sample size or an effect size that is smaller than anticipated. *Design bias* - **Design bias** refers to systematic errors introduced by the way a study is planned or executed, such as inappropriate blinding, randomization, or choice of control group. - While it can lead to incorrect conclusions, it usually results in detecting an effect that isn't real (Type I error) or masking an effect due to fundamental flaws, rather than simply failing to detect a true effect due to power issues, which is inferred by the second team finding the effect. *Type I error* - A **Type I error** (or **alpha error**) occurs when a study concludes there is an effect when, in reality, there is none (a **false positive**). - This scenario describes the opposite: the first study concluded no effect, but subsequent research proved there was one. *Selection bias* - **Selection bias** occurs when the study participants are not representative of the target population, leading to results that cannot be generalized. - While selection bias can impact study outcomes, the problem described is more about the study's ability to detect a true effect, rather than the representativeness of the sample distorting the effect itself. *Type III error* - A **Type III error** occurs when a researcher correctly rejects the null hypothesis but for the wrong reason or by incorrectly interpreting the nature of the effect. - This is not applicable here as the first study *failed* to reject the null hypothesis, rather than rejecting it for the wrong reason.

Question 21: A medical research study is evaluating an investigational novel drug (medication 1) as compared with standard therapy (medication 2) in patients presenting to the emergency department with myocardial infarction (MI). The study enrolled a total of 3,000 subjects, 1,500 in each study arm. Follow-up was conducted at 45 days post-MI. The following are the results of the trial: Endpoints Medication 1 Medication 2 P-Value Primary: death from cardiac causes 134 210 0.03 Secondary: hyperkalemia 57 70 0.4 What is the relative risk of death from a cardiac cause, expressed as a percentage? (Round to the nearest whole number.)

A. 64% (Correct Answer)
B. 42%
C. 72%
D. 36%
E. 57%

Explanation: ***64%*** - The **relative risk (RR)** is calculated as the event rate in the exposed group divided by the event rate in the unexposed (control) group. - For cardiac death, the event rate for Medication 1 is 134/1500 = 0.0893, and for Medication 2 is 210/1500 = 0.14. Therefore, RR = 0.0893 / 0.14 = 0.6378. - Expressing as a percentage: 0.6378 × 100 = 63.78%, which rounds to **64%**. - This indicates that Medication 1 has 64% of the risk of cardiac death compared to Medication 2, representing a **36% relative risk reduction**. *42%* - This option is incorrect as it does not reflect the accurate calculation of **relative risk** using the provided event rates. - A calculation error or conceptual misunderstanding of the relative risk formula would lead to this value. *72%* - This percentage is higher than the calculated relative risk, suggesting an incorrect application of the formula or a misinterpretation of the event rates. - It does not represent the ratio of risk between the two medication groups for cardiac death. *36%* - This value represents the **relative risk reduction** (100% - 64% = 36%), not the relative risk itself. - This is a common error where students confuse relative risk with relative risk reduction. *57%* - While closer to the correct answer, this value is not the precise result when rounding to the nearest whole number. - Small calculation discrepancies or rounding at intermediate steps could lead to this slightly different percentage.

Question 22: A researcher is trying to determine whether a newly discovered substance X can be useful in promoting wound healing after surgery. She conducts this study by enrolling the next 100 patients that will be undergoing this surgery and separating them into 2 groups. She decides which patient will be in which group by using a random number generator. Subsequently, she prepares 1 set of syringes with the novel substance X and 1 set of syringes with a saline control. Both of these sets of syringes are unlabeled and the substances inside cannot be distinguished. She gives the surgeon performing the surgery 1 of the syringes and does not inform him nor the patient which syringe was used. After the study is complete, she analyzes all the data that was collected and performs statistical analysis. This study most likely provides which level of evidence for use of substance X?

A. Level 3
B. Level 1 (Correct Answer)
C. Level 4
D. Level 5
E. Level 2

Explanation: ***Level 1*** - The study design described is a **randomized controlled trial (RCT)**, which is considered the **highest level of evidence (Level 1)** in the hierarchy of medical evidence. - Key features like **randomization**, **control group**, and **blinding (double-blind)** help minimize bias and strengthen the validity of the findings. *Level 2* - Level 2 evidence typically comprises **well-designed controlled trials without randomization** (non-randomized controlled trials) or **high-quality cohort studies**. - While strong, they do not possess the same level of internal validity as randomized controlled trials. *Level 3* - Level 3 evidence typically includes **case-control studies** or **cohort studies**, which are observational designs and carry a higher risk of bias compared to RCTs. - These studies generally do not involve randomization or intervention assignment by the researchers. *Level 4* - Level 4 evidence is usually derived from **case series** or **poor quality cohort and case-control studies**. - These studies provide descriptive information or investigate associations without strong control for confounding factors. *Level 5* - Level 5 evidence is the **lowest level of evidence**, consisting of **expert opinion** or **animal research/bench research**. - This level lacks human clinical data or systematic investigative rigor needed for higher evidence levels.

Question 23: A pharmaceutical company conducts a randomized clinical trial in an attempt to show that their new anticoagulant drug prevents more thrombotic events following total knee arthroplasty than the current standard of care. However, a significant number of patients are lost to follow-up or fail to complete treatment according to the study arm to which they were assigned. Several patients in the novel drug arm are also switched at a later time to a novel anticoagulant or warfarin per their primary care physician. All patients enrolled in the study are subsequently analyzed based on the initial group they were assigned to and there is a significant improvement in outcome of the new drug. What analysis most appropriately describes this trial?

A. Per protocol
B. As treated
C. Non-inferiority
D. Intention to treat (Correct Answer)
E. Modified intention to treat

Explanation: ***Intention to treat*** - **Intention-to-treat (ITT)** analysis includes all participants randomized to a treatment arm, regardless of whether they completed the intervention or switched treatments, reflecting a real-world scenario and preserving randomization benefits. - This approach minimizes bias from **loss to follow-up** or **treatment crossovers** and provides a more conservative estimate of treatment effect. *Per protocol* - **Per-protocol analysis** only includes participants who completed the study exactly as planned without any deviations. - This method is susceptible to **selection bias** because it excludes patients who may have experienced adverse events or treatment failures, potentially overestimating treatment efficacy. *As treated* - **As-treated analysis** analyzes patients based on the actual treatment received, rather than the treatment to which they were randomized. - This approach can introduce **confounding** and selection bias, as patients who switch treatments may do so for reasons related to their prognosis or treatment response. *Non-inferiority* - A **non-inferiority trial** design aims to show that a new treatment is not appreciably worse than an active control, rather than proving superiority. - This describes a **type of study design** or hypothesis, not an analysis method for handling patient data after randomization with non-adherence. *Modified intention to treat* - A **modified intention-to-treat (mITT)** analysis typically excludes a small, predefined group of patients from the ITT population, such as those who never received any study drug or were found to be ineligible after randomization. - While similar to ITT, it involves specific exclusions that are not described in this scenario, where all randomized patients were analyzed **based on initial assignment**.

Question 24: A pharmaceutical company conducts a randomized clinical trial to demonstrate that their new anticoagulant drug, Aclotsaban, prevents more thrombotic events following total knee arthroplasty than the current standard of care. A significant number of patients are lost to follow-up, and many fail to complete treatment according to the study arm to which they were assigned. Despite these protocol deviations, the results for the patients who completed the course of Aclotsaban are encouraging. Which of the following analytical approaches is most appropriate for the primary analysis to establish the efficacy of Aclotsaban?

A. Intention-to-treat analysis (Correct Answer)
B. Sub-group analysis
C. Per-protocol analysis
D. As-treated analysis
E. Non-inferiority analysis

Explanation: ***Intention-to-treat analysis*** - **Intention-to-treat (ITT) analysis** is the gold standard for the **primary analysis in superiority trials** and includes all patients in the groups to which they were originally randomized, regardless of protocol deviations, loss to follow-up, or treatment discontinuation. - ITT preserves **randomization balance**, prevents bias from selective dropout (patients may drop out due to adverse effects or lack of efficacy), and provides a **conservative, realistic estimate** of treatment effect in actual clinical practice. - For **regulatory approval and establishing efficacy**, ITT is the most appropriate primary analysis method even when dropout rates are high, as it maintains the integrity of the randomized comparison. *Per-protocol analysis* - **Per-protocol analysis** includes only patients who completed the study exactly as planned without protocol deviations. - While the encouraging results in completers are noted, per-protocol analysis can **introduce significant bias** by excluding patients who dropped out due to adverse events or lack of efficacy, potentially **overestimating treatment benefit**. - Per-protocol is typically used as a **secondary/supportive analysis**, not the primary method for establishing superiority. *As-treated analysis* - **As-treated analysis** categorizes patients according to the treatment they actually received rather than their randomized assignment. - This violates the principle of randomization and can introduce **confounding bias**, as actual treatment received may be influenced by prognostic factors. *Sub-group analysis* - **Sub-group analysis** evaluates treatment effects within specific patient subsets. - This is **hypothesis-generating** rather than confirmatory and increases the risk of false-positive findings (multiple comparisons problem) unless pre-specified in the protocol. *Non-inferiority analysis* - **Non-inferiority analysis** tests whether a new treatment is not worse than control by more than a pre-specified margin. - The goal here is to demonstrate **superiority** (better than standard care), not non-inferiority, making this approach inappropriate.

Question 25: A group of investigators have conducted a randomized clinical trial to evaluate the efficacy of adding a novel adenosine A1 receptor agonist to the standard anti-epileptic treatment in reducing the frequency of focal seizures. It was found that patients taking the combination regimen (n = 200) had a lower seizure frequency compared to patients taking the standard treatment alone (n = 200; p < 0.01). However, several participants taking the novel drug reported severe drowsiness. The investigators administered a survey to both the combination treatment group and standard treatment group to evaluate whether the drowsiness interfered with daily functioning using a yes or no questionnaire. Results are shown: Interference with daily functioning Yes (number of patients) No (number of patients) Combination treatment group 115 85 Standard treatment group 78 122 Which of the following statistical methods would be most appropriate for assessing the statistical significance of these results?

A. Paired t-test
B. Unpaired t-test
C. Chi-square test (Correct Answer)
D. Analysis of variance
E. Multiple linear regression

Explanation: ***Chi-square test*** - The **chi-square test** is appropriate for comparing **categorical data** (yes/no responses) between two or more independent groups. - The data presented (number of patients endorsing "yes" or "no" for interference with daily functioning in two different treatment groups) perfectly fits this scenario. *Paired t-test* - A **paired t-test** is used to compare means of two related (or dependent) samples, such as measurements taken on the same subjects before and after an intervention. - This scenario involves two independent groups, not repeated measures on the same subjects. *Unpaired t-test* - An **unpaired t-test** (also known as an independent samples t-test) is used to compare the means of two independent groups for a **continuous outcome variable**. - Here, the outcome variable ("interference with daily functioning") is categorical (yes/no), not continuous. *Analysis of variance* - **ANOVA** is used to compare the means of **three or more independent groups** for a **continuous outcome variable**. - This study involves only two groups and a categorical outcome, making ANOVA unsuitable. *Multiple linear regression* - **Multiple linear regression** is used to model the relationship between a **continuous dependent variable** and two or more independent variables (either continuous or categorical). - The outcome variable in this case is categorical, not continuous, making linear regression inappropriate.

Question 26: A group of investigators conducted a randomized controlled trial to compare the effectiveness of rivaroxaban to warfarin for ischemic stroke prevention in patients with atrial fibrillation. A total of 14,000 participants were enrolled and one half was assigned to each of the cohorts. The patients were followed prospectively for 3 years. At the conclusion of the trial, the incidence of ischemic stroke in participants taking rivaroxaban was 1.7% compared to 2.2% in participants taking warfarin. The hazard ratio is calculated as 0.79 and the 95% confidence interval is reported as 0.64 to 0.97. If the study was conducted with a total of 7,000 participants, which of the following changes would most be expected?

A. Increased risk of selection bias
B. Decreased type I error rate
C. Decreased hazard ratio
D. Increased confidence interval range (Correct Answer)
E. Increased risk of confounding bias

Explanation: ***Increased confidence interval range*** - A smaller sample size (7,000 instead of 14,000) reduces the **precision** of the study's estimates, leading to a wider **confidence interval (CI)**. - A wider CI reflects greater **uncertainty** around the true effect size of the intervention. *Increased risk of selection bias* - **Randomized controlled trials (RCTs)** inherently minimize selection bias by randomly assigning participants to treatment groups, regardless of sample size. - The risk of selection bias is primarily addressed by the study design, not directly by the sample size if randomization is maintained. *Decreased type I error rate* - The **Type I error rate (alpha level)**, typically set at 0.05, is an a priori decision made by investigators and does not change based on sample size. - A smaller sample size would likely **increase the Type II error rate** (failing to detect a true difference) due to reduced power. *Decreased hazard ratio* - The **hazard ratio (HR)** is a measure of the relative effect of the intervention on an outcome and is an estimate derived from the observed data. - While a smaller sample size can lead to more variability in the HR estimate, it does not inherently mean the hazard ratio itself would decrease. *Increased risk of confounding bias* - **Randomization** in a well-conducted RCT helps to distribute known and unknown confounders evenly between study groups, regardless of sample size. - The primary method to control for confounding is the study design (randomization), not the number of participants.

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Sample size determination

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Inclusion and exclusion criteria

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Primary vs secondary outcomes

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Interim analyses

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Intention-to-treat analysis

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Per-protocol analysis

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Crossover designs

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Factorial designs

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Cluster randomization

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