An investigator studying the epidemiology of breast cancer finds that prevalence of breast cancer has increased significantly in the United States since the 1980s. After analyzing a number of large epidemiological surveillance databases, the epidemiologist notices that the incidence of breast cancer has remained relatively stable over the past 30 years. Which of the following best explains these epidemiological trends?
Q32
A scientist is studying the characteristics of a newly discovered infectious disease in order to determine its features. He calculates the number of patients that develop the disease over several months and finds that on average 75 new patients become infected per month. Furthermore, he knows that the disease lasts on average 2 years before patients are either cured or die from the disease. If the population being studied consists of 7500 individuals, which of the following is the prevalence of the disease?
Q33
A 50-year-old man presents to the office for a routine health check-up. Managing his weight has been his focus to improve his overall health. The doctor discusses his weight loss goals and overall health benefits from weight loss, including better blood pressure management and decreased insulin resistance. The national average weight for males aged 50-59 years old is 90 kg (200 lb) with a standard deviation of 27 kg (60 lb). What would be the most likely expected value if his weight was 2 standard deviations above the mean?
Q34
An investigator for a nationally representative health survey is evaluating the heights and weights of men and women aged 18–74 years in the United States. The investigator finds that for each sex, the distribution of heights is well-fitted by a normal distribution. The distribution of weight is not normally distributed. Results are shown:
Mean Standard deviation
Height (inches), men 69 0.1
Height (inches), women 64 0.1
Weight (pounds), men 182 1.0
Weight (pounds), women 154 1.0
Based on these results, which of the following statements is most likely to be correct?
Q35
A clinical trial investigating a new biomedical device used to correct congenital talipes equinovarus (club foot) in infants has recently been published. The study was a preliminary investigation of a new device and as such the sample size is only 20 participants. The results indicate that the new biomedical device is less efficacious than the current standard of care of serial casting (p < 0.001), but the authors mention in the conclusion that it may be due to a single outlier--a patient whose foot remained uncorrected by the conclusion of the study. Which of the following descriptive statistics is the least sensitive to outliers?
Q36
A research study is comparing 2 novel tests for the diagnosis of Alzheimer’s disease (AD). The first is a serum blood test, and the second is a novel PET radiotracer that binds to beta-amyloid plaques. The researchers intend to have one group of patients with AD assessed via the novel blood test, and the other group assessed via the novel PET examination. In comparing these 2 trial subsets, the authors of the study may encounter which type of bias?
Q37
The APPLE study investigators are currently preparing for a 30-year follow-up evaluation. They are curious about the number of participants who will partake in follow-up interviews. The investigators noted that of the 83 participants who participated in the APPLE study's 20-year follow-up, 62 were in the treatment group and 21 were in the control group. Given the unequal distribution of participants between groups at follow-up, this finding raises concerns for which of the following?
Q38
An endocrine surgeon wants to evaluate the risk of multiple endocrine neoplasia (MEN) type 2 syndromes in patients who experienced surgical hypertension during pheochromocytoma resection. She conducts a case-control study that identifies patients who experienced surgical hypertension and subsequently compares them to the control group with regard to the number of patients with underlying MEN type 2 syndromes. The odds ratio of MEN type 2 syndromes in patients with surgical hypertension during pheochromocytoma removal was 3.4 (p < 0.01). Given the rare disease assumption, this odds ratio can be interpreted as an approximation of the relative risk. The surgeon concludes that the risk of surgical hypertension during pheochromocytoma removal is 3.4 times greater in patients with MEN type 2 syndromes than in patients without MEN syndromes. This conclusion is best supported by which of the following assumptions?
Q39
An investigator conducts a case-control study to evaluate the relationship between benzodiazepine use among the elderly population (older than 65 years of age) that resides in assisted-living facilities and the risk of developing Alzheimer dementia. Three hundred patients with Alzheimer dementia are recruited from assisted-living facilities throughout the New York City metropolitan area, and their rates of benzodiazepine use are compared to 300 controls. Which of the following describes a patient who would be appropriate for the study's control group?
Q40
An investigator is measuring the blood calcium level in a sample of female cross country runners and a control group of sedentary females. If she would like to compare the means of the two groups, which statistical test should she use?
Study Design US Medical PG Practice Questions and MCQs
Question 31: An investigator studying the epidemiology of breast cancer finds that prevalence of breast cancer has increased significantly in the United States since the 1980s. After analyzing a number of large epidemiological surveillance databases, the epidemiologist notices that the incidence of breast cancer has remained relatively stable over the past 30 years. Which of the following best explains these epidemiological trends?
A. Increased average age of population at risk for breast cancer
B. Improved treatment of breast cancer (Correct Answer)
C. Increased awareness of breast cancer among clinicians
D. Improved screening programs for breast cancer
E. Increased exposure to risk factors for breast cancer
Explanation: ***Improved treatment of breast cancer***
- An **increased prevalence** with **stable incidence** suggests that people are living longer with the disease.
- More effective treatments allow individuals with breast cancer to survive for extended periods, contributing to a larger pool of existing cases.
*Increased average age of population at risk for breast cancer*
- While an increase in the average age of the population could lead to more cases due to age being a risk factor, this would primarily impact **incidence**, not just prevalence with stable incidence.
- If incidence remained stable despite an aging population, it wouldn't fully explain the observed increase in prevalence.
*Increased awareness of breast cancer among clinicians*
- Increased awareness would likely lead to earlier diagnosis, which might temporarily show an increase in **incidence** due to detection of previously undiagnosed cases.
- However, it wouldn't directly explain a sustained increase in prevalence without a change in incidence or survival.
*Improved screening programs for breast cancer*
- Better screening programs would detect more cases, leading to an initial increase in **incidence** (identifying cases earlier) rather than stable incidence.
- While it contributes to earlier diagnosis, it doesn't primarily explain why more people are living longer with the disease, thus increasing prevalence.
*Increased exposure to risk factors for breast cancer*
- An increase in risk factors would typically lead to an increase in the **incidence** of breast cancer, as more people would be developing the disease.
- The question explicitly states that the incidence has remained relatively stable, making this option less likely.
Question 32: A scientist is studying the characteristics of a newly discovered infectious disease in order to determine its features. He calculates the number of patients that develop the disease over several months and finds that on average 75 new patients become infected per month. Furthermore, he knows that the disease lasts on average 2 years before patients are either cured or die from the disease. If the population being studied consists of 7500 individuals, which of the following is the prevalence of the disease?
A. 0.24 (Correct Answer)
B. 0.02
C. 0.12
D. 0.005
E. 0.01
Explanation: ***0.24***
- Prevalence is calculated as the **number of existing cases** divided by the **total population**. The number of existing cases is estimated by multiplying the **incidence rate** (75 new cases/month) by the **duration of the disease** (2 years or 24 months): 75 cases/month * 24 months = 1800 cases.
- The prevalence is then 1800 cases / 7500 individuals = **0.24**.
*0.02*
- This value might be obtained by incorrectly using only the monthly incidence or by performing **incorrect calculations** involving the duration and total population.
- It does not account for the **cumulative effect** of new cases over the entire disease duration.
*0.12*
- This answer might result from miscalculating the **duration of the disease** (e.g., using 1 year instead of 2 years), leading to an underestimation of the total existing cases.
- It suggests an error in converting the **duration from years to months** when multiplying by the monthly incidence.
*0.005*
- This value is significantly lower than the correct prevalence, suggesting a major error in calculating the total number of cases or incorrectly dividing the total cases by the entire population.
- It does not properly reflect the contribution of new cases over the **duration of the disease**.
*0.01*
- This result is likely derived from an incorrect application of the incidence rate or a misunderstanding of how the duration of the disease impacts the **total number of prevalent cases**.
- It's a calculation error that significantly underestimates the **true disease burden**.
Question 33: A 50-year-old man presents to the office for a routine health check-up. Managing his weight has been his focus to improve his overall health. The doctor discusses his weight loss goals and overall health benefits from weight loss, including better blood pressure management and decreased insulin resistance. The national average weight for males aged 50-59 years old is 90 kg (200 lb) with a standard deviation of 27 kg (60 lb). What would be the most likely expected value if his weight was 2 standard deviations above the mean?
A. 36 kg (80 lb)
B. 63 kg (140 lb)
C. 172 kg (380 lb)
D. 144 kg (320 lb) (Correct Answer)
E. 118 kg (260 lb)
Explanation: ***144 kg (320 lb)***
- To find a weight 2 standard deviations above the mean, you use the formula: **mean + (2 × standard deviation)**.
- Given a mean of 90 kg and a standard deviation of 27 kg, the calculation is 90 + (2 × 27) = 90 + 54 = **144 kg**. In pounds: 200 lb + (2 × 60 lb) = 200 + 120 = **320 lb**.
*36 kg (80 lb)*
- This value is significantly below the mean and represents a weight **2 standard deviations below the mean**, not above it.
- Calculation: 90 - (2 × 27) = 90 - 54 = 36 kg.
*63 kg (140 lb)*
- This value is **below the mean** and represents a weight approximately **1 standard deviation below the mean**, not above.
- Calculation: 90 - 27 = 63 kg.
*172 kg (380 lb)*
- This value is **too high** for 2 standard deviations above the mean and would represent a weight closer to **3 standard deviations above the mean**.
- Calculation: 90 + (3 × 27) = 90 + 81 = 171 kg (approximately 172 kg).
*118 kg (260 lb)*
- This value represents a weight approximately **1 standard deviation above the mean**, not 2.
- Calculation: 90 + 27 = 117 kg (approximately 118 kg or 260 lb).
Question 34: An investigator for a nationally representative health survey is evaluating the heights and weights of men and women aged 18–74 years in the United States. The investigator finds that for each sex, the distribution of heights is well-fitted by a normal distribution. The distribution of weight is not normally distributed. Results are shown:
Mean Standard deviation
Height (inches), men 69 0.1
Height (inches), women 64 0.1
Weight (pounds), men 182 1.0
Weight (pounds), women 154 1.0
Based on these results, which of the following statements is most likely to be correct?
A. 86% of heights in women are likely to fall between 63.9 and 64.1 inches.
B. 99.7% of heights in women are likely to fall between 63.7 and 64.3 inches. (Correct Answer)
C. 68% of weights in women are likely to fall between 153 and 155 pounds.
D. 95% of heights in men are likely to fall between 68.85 and 69.15 inches.
E. 99.7% of heights in men are likely to fall between 68.8 and 69.2 inches.
Explanation: ***99.7% of heights in women are likely to fall between 63.7 and 64.3 inches.***
* For a **normal distribution**, approximately 99.7% of values fall within **±3 standard deviations** of the mean.
* For women's height: Mean = 64 inches, Standard Deviation = 0.1 inches. Therefore, 3 SD = 0.3 inches. The range is 64 ± 0.3, which is **63.7 to 64.3 inches**.
*86% of heights in women are likely to fall between 63.9 and 64.1 inches.*
* The range 63.9 to 64.1 inches represents **±1 standard deviation** (64 ± 0.1 inches).
* For a normal distribution, approximately **68%** (not 86%) of values fall within ±1 standard deviation of the mean.
*68% of weights in women are likely to fall between 153 and 155 pounds.*
* While 153 to 155 pounds represents **±1 standard deviation** (154 ± 1 pound), the problem states that the **distribution of weight is not normally distributed**.
* The **68-95-99.7 rule** (empirical rule) only applies to data that follows a normal distribution.
*95% of heights in men are likely to fall between 68.85 and 69.15 inches.*
* For a normal distribution, 95% of values fall within **±2 standard deviations**.
* For men's height: Mean = 69 inches, Standard Deviation = 0.1 inches. Therefore, 2 SD = 0.2 inches. The range for 95% should be 69 ± 0.2, which is **68.8 to 69.2 inches**, not 68.85 to 69.15 inches.
*99.7% of heights in men are likely to fall between 68.8 and 69.2 inches.*
* For a normal distribution, 99.7% of values fall within **±3 standard deviations**.
* For men's height: Mean = 69 inches, Standard Deviation = 0.1 inches. Therefore, 3 SD = 0.3 inches. The range for 99.7% should be 69 ± 0.3, which is **68.7 to 69.3 inches**, not 68.8 to 69.2 inches.
Question 35: A clinical trial investigating a new biomedical device used to correct congenital talipes equinovarus (club foot) in infants has recently been published. The study was a preliminary investigation of a new device and as such the sample size is only 20 participants. The results indicate that the new biomedical device is less efficacious than the current standard of care of serial casting (p < 0.001), but the authors mention in the conclusion that it may be due to a single outlier--a patient whose foot remained uncorrected by the conclusion of the study. Which of the following descriptive statistics is the least sensitive to outliers?
A. Standard deviation
B. Median (Correct Answer)
C. Mean
D. Variance
E. Mode
Explanation: ***Median***
- The **median** is the middle value in a dataset when ordered from least to greatest, making it inherently resistant to extreme values or **outliers**.
- It describes the central tendency without being skewed by a single unusually high or low data point, unlike the mean.
- Among measures of central tendency, the median is the **most robust** to outliers.
*Standard deviation*
- **Standard deviation** measures the spread of data points around the mean, and because it is based on the **mean**, it is highly sensitive to outliers.
- A single outlier can significantly increase the standard deviation, making the data appear more dispersed than it actually is for the majority of observations.
*Mean*
- The **mean** is calculated by summing all values and dividing by the number of values, which makes it directly affected by every data point, especially extreme ones.
- A single **outlier** can pull the mean significantly towards its value, misrepresenting the central tendency of the majority of the data.
*Variance*
- **Variance** is the average of the squared differences from the mean, and like standard deviation, its calculation heavily relies on the **mean**.
- Squaring the differences amplifies the impact of outliers, making variance very sensitive to extreme values.
*Mode*
- The **mode** represents the most frequently occurring value in a dataset and is also resistant to outliers since it only depends on frequency of occurrence.
- However, in small datasets or datasets without repeated values, the mode may be **undefined or uninformative**, making it less useful for describing central tendency compared to the median.
Question 36: A research study is comparing 2 novel tests for the diagnosis of Alzheimer’s disease (AD). The first is a serum blood test, and the second is a novel PET radiotracer that binds to beta-amyloid plaques. The researchers intend to have one group of patients with AD assessed via the novel blood test, and the other group assessed via the novel PET examination. In comparing these 2 trial subsets, the authors of the study may encounter which type of bias?
A. Selection bias (Correct Answer)
B. Confounding bias
C. Recall bias
D. Measurement bias
E. Lead-time bias
Explanation: ***Selection bias***
- This occurs when different patient groups are assigned to different interventions or measurements in a way that creates **systematic differences** between comparison groups.
- In this study, having **separate patient groups** assessed with different diagnostic methods (blood test vs. PET scan) means any differences observed could be due to **differences in the patient populations** rather than differences in test performance.
- To validly compare two diagnostic tests, both tests should ideally be performed on the **same patients** (paired design) or patients should be **randomly assigned** to receive one test or the other, ensuring comparable groups.
- This is a fundamental **study design flaw** that prevents valid comparison of the two diagnostic methods.
*Measurement bias*
- Also called information bias, this occurs when there are systematic errors in how outcomes or exposures are measured.
- While using different measurement tools could introduce measurement variability, the primary issue here is that **different patient populations** are being compared, not just different measurement methods on the same population.
- Measurement bias would be more relevant if the same patients were assessed with both methods but one method was systematically misapplied or measured incorrectly.
*Confounding bias*
- This occurs when an extraneous variable is associated with both the exposure and outcome, distorting the observed relationship.
- While patient characteristics could confound results, the fundamental problem is the **study design itself** (separate groups for separate tests), which is selection bias.
*Recall bias*
- This involves systematic differences in how participants remember or report past events, common in **retrospective case-control studies**.
- Not relevant here, as this involves prospective diagnostic testing, not recollection of past exposures.
*Lead-time bias*
- Occurs in screening studies when earlier detection makes survival appear longer without changing disease outcomes.
- Not applicable to this scenario, which focuses on comparing two diagnostic methods in separate patient groups, not on survival or disease progression timing.
Question 37: The APPLE study investigators are currently preparing for a 30-year follow-up evaluation. They are curious about the number of participants who will partake in follow-up interviews. The investigators noted that of the 83 participants who participated in the APPLE study's 20-year follow-up, 62 were in the treatment group and 21 were in the control group. Given the unequal distribution of participants between groups at follow-up, this finding raises concerns for which of the following?
A. Volunteer bias
B. Reporting bias
C. Inadequate sample size
D. Attrition bias (Correct Answer)
E. Lead-time bias
Explanation: ***Attrition bias***
- **Attrition bias** occurs when participants drop out of a study, especially if the dropout rate differs between the intervention and control groups, which can lead to a **skewed comparison** of outcomes.
- The unequal distribution of participants (62 vs. 21) between the treatment and control groups at the 20-year follow-up suggests that a disproportionate number of participants may have dropped out of one group, thus leading to attrition bias.
*Volunteer bias*
- **Volunteer bias** occurs when individuals who volunteer for a study differ significantly from the general population or those who decline to participate, potentially affecting the study's **generalizability**.
- This scenario describes differences in retention *after* initial participation, not differences in initial willingness to join.
*Reporting bias*
- **Reporting bias** refers to the selective reporting of study findings, where positive or statistically significant results are more likely to be published or emphasized than negative or non-significant ones, which can distort the overall evidence base.
- This bias relates to how results are disseminated, not to differential dropout rates or participant retention in a study.
*Inadequate sample size*
- **Inadequate sample size** means that the number of participants in a study is too small to detect a statistically significant effect if one truly exists, leading to a lack of **statistical power**.
- While the overall number of participants at follow-up might be small, the primary concern here is the *unequal distribution* between groups, indicating a problem with participant retention rather than just a low total count.
*Lead-time bias*
- **Lead-time bias** occurs when early detection of a disease (e.g., through screening) makes survival appear longer than it actually is, without necessarily prolonging the patient's life, by advancing the **point of diagnosis**.
- This bias is relevant to screening programs and disease detection, not to the differential dropout rates observed in a longitudinal study.
Question 38: An endocrine surgeon wants to evaluate the risk of multiple endocrine neoplasia (MEN) type 2 syndromes in patients who experienced surgical hypertension during pheochromocytoma resection. She conducts a case-control study that identifies patients who experienced surgical hypertension and subsequently compares them to the control group with regard to the number of patients with underlying MEN type 2 syndromes. The odds ratio of MEN type 2 syndromes in patients with surgical hypertension during pheochromocytoma removal was 3.4 (p < 0.01). Given the rare disease assumption, this odds ratio can be interpreted as an approximation of the relative risk. The surgeon concludes that the risk of surgical hypertension during pheochromocytoma removal is 3.4 times greater in patients with MEN type 2 syndromes than in patients without MEN syndromes. This conclusion is best supported by which of the following assumptions?
A. The case-control study used a large sample size
B. The relationship between MEN syndromes and surgical hypertension is not due to random error
C. Pheochromocytoma is common in MEN type 2 syndromes
D. The 95% confidence interval for the odds ratio does not include 1.0
E. Surgical hypertension associated with pheochromocytoma is rare (Correct Answer)
Explanation: ***Surgical hypertension associated with pheochromocytoma is rare***
- The phrase "given the **rare disease assumption**" is critical here, as it allows the **odds ratio** to approximate the **relative risk**. This assumption is valid when the outcome (surgical hypertension) is rare in the general population.
- If the outcome is rare, the odds ratio provides a good estimate of how many times more likely the outcome is in the exposed group compared to the unexposed group.
*The case-control study used a large sample size*
- A large sample size increases the **precision** of the estimate and the **statistical power** but does not inherently allow an odds ratio to be interpreted as a relative risk.
- While important for reliable results, sample size alone doesn't validate the "rare disease assumption."
*The relationship between MEN syndromes and surgical hypertension is not due to random error*
- This statement refers to the **statistical significance** of the findings (p < 0.01), indicating the observed effect is unlikely due to chance.
- It does not, however, relate to the specific condition under which an odds ratio approximates a relative risk.
*Pheochromocytoma is common in MEN type 2 syndromes*
- This statement addresses the prevalence of pheochromocytoma in patients with MEN type 2, not the rarity of the outcome (surgical hypertension) in the general population.
- While pheochromocytoma is indeed a feature of MEN2, this fact alone doesn't validate the rare disease assumption regarding surgical hypertension.
*The 95% confidence interval for the odds ratio does not include 1.0*
- This indicates **statistical significance**, meaning the odds ratio is significantly different from 1, suggesting an association between the exposure and the outcome.
- It does not provide the basis for interpreting the odds ratio as a relative risk under the rare disease assumption.
Question 39: An investigator conducts a case-control study to evaluate the relationship between benzodiazepine use among the elderly population (older than 65 years of age) that resides in assisted-living facilities and the risk of developing Alzheimer dementia. Three hundred patients with Alzheimer dementia are recruited from assisted-living facilities throughout the New York City metropolitan area, and their rates of benzodiazepine use are compared to 300 controls. Which of the following describes a patient who would be appropriate for the study's control group?
A. A 73-year-old woman with coronary artery disease who was recently discharged to an assisted-living facility from the hospital after a middle cerebral artery stroke
B. An 86-year-old man with well-controlled hypertension and mild benign prostate hyperplasia who lives in an assisted-living facility (Correct Answer)
C. A 64-year-old man with well-controlled hypertension and mild benign prostate hyperplasia who lives in an assisted-living facility
D. An 80-year-old man with well-controlled hypertension and mild benign prostate hyperplasia who lives in an independent-living community
E. A 68-year-old man with hypercholesterolemia, mild benign prostate hyperplasia, and poorly-controlled diabetes who is hospitalized for pneumonia
Explanation: ***An 86-year-old man with well-controlled hypertension and mild benign prostate hyperplasia who lives in an assisted-living facility***
- This patient meets all criteria stipulated for the control group: **older than 65 years of age**, and **resides in an assisted-living facility**.
- They also have no mention of dementia, making them suitable as a **healthy control** for the study.
*A 73-year-old woman with coronary artery disease who was recently discharged to an assisted-living facility from the hospital after a middle cerebral artery stroke*
- Although this patient is over 65 and in an assisted-living facility, a recent **middle cerebral artery stroke** could lead to **vascular cognitive impairment**, which might confound the assessment of Alzheimer's dementia.
- Controls should ideally be free of conditions that could mimic or predispose to dementia, complicating the analysis of the association with benzodiazepine use.
*A 64-year-old man with well-controlled hypertension and mild benign prostate hyperplasia who lives in an assisted-living facility*
- This patient does not meet the specified age criterion of being **older than 65 years of age**.
- All participants in the study, including controls, must be 65 years or older to maintain the integrity of the study population.
*An 80-year-old man with well-controlled hypertension and mild benign prostate hyperplasia who lives in an independent-living community*
- This patient does not reside in an **assisted-living facility**, which is a crucial inclusion criterion for all participants in this study.
- The study specifically focuses on the elderly population residing in **assisted-living facilities** to ensure a uniform study environment.
*A 68-year-old man with hypercholesterolemia, mild benign prostate hyperplasia, and poorly-controlled diabetes who is hospitalized for pneumonia*
- This patient is currently **hospitalized for pneumonia**, indicating an acute illness that would make them unsuitable for selection into a control group for a chronic disease study.
- Controls should be relatively healthy and stable; acute hospitalization suggests a compromised health state not representative of the target control population.
Question 40: An investigator is measuring the blood calcium level in a sample of female cross country runners and a control group of sedentary females. If she would like to compare the means of the two groups, which statistical test should she use?
A. Chi-square test
B. Linear regression
C. t-test (Correct Answer)
D. ANOVA (Analysis of Variance)
E. F-test
Explanation: ***t-test***
- A **t-test** is appropriate for comparing the means of two independent groups, such as the blood calcium levels between runners and sedentary females.
- It assesses whether the observed difference between the two sample means is statistically significant or occurred by chance.
*Chi-square test*
- The **chi-square test** is used to analyze categorical data to determine if there is a significant association between two variables.
- It is not suitable for comparing continuous variables like blood calcium levels.
*Linear regression*
- **Linear regression** is used to model the relationship between a dependent variable (outcome) and one or more independent variables (predictors).
- It aims to predict the value of a variable based on the value of another, rather than comparing means between groups.
*ANOVA (Analysis of Variance)*
- **ANOVA** is used to compare the means of **three or more independent groups**.
- Since there are only two groups being compared in this scenario, a t-test is more specific and appropriate.
*F-test*
- The **F-test** is primarily used to compare the variances of two populations or to assess the overall significance of a regression model.
- While it is the basis for ANOVA, it is not the direct test for comparing the means of two groups.
