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?
Q2
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?
Q3
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)?
Q4
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?
Q5
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?
Q6
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?
Q7
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?
Q8
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?
Q9
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.
