Case-control studies US Medical PG Practice Questions and MCQs
Practice US Medical PG questions for Case-control studies. These multiple choice questions (MCQs) cover important concepts and help you prepare for your exams.
Case-control studies US Medical PG Question 1: A researcher is studying whether a new knee implant is better than existing alternatives in terms of pain after knee replacement. She designs the study so that it includes all the surgeries performed at a certain hospital. Interestingly, she notices that patients who underwent surgeries on Mondays and Thursdays reported much better pain outcomes on a survey compared with those who underwent the same surgeries from the same surgeons on Tuesdays and Fridays. Upon performing further analysis, she discovers that one of the staff members who works on Mondays and Thursdays is aware of the study and tells all the patients about how wonderful the new implant is. Which of the following forms of bias does this most likely represent?
- A. Hawthorne effect
- B. Pygmalion effect (Correct Answer)
- C. Attrition bias
- D. Golem effect
Case-control studies Explanation: ***Pygmalion effect***
- This bias occurs when higher expectations lead to an increase in performance. In this scenario, the staff member's positive reinforcement about the new implant likely instilled **higher patient expectations**, leading to better reported pain outcomes.
- The patients' belief in the implant's superiority, influenced by the staff member, acted as a **self-fulfilling prophecy**, improving their subjective pain experience.
*Hawthorne effect*
- This effect describes how individuals modify an aspect of their behavior in response to their awareness of being observed. While patients were part of a study, their improved outcomes were specifically linked to a staff member's verbal influence, not solely the act of observation.
- The improved pain outcomes stem from the **expectations created by the staff member's praise**, rather than a general awareness of being studied.
*Attrition bias*
- Attrition bias refers to systematic differences between groups in the loss of participants from a study.
- This scenario describes differences in patient outcomes based on staff influence during the study, not due to **patients dropping out differentially** between groups.
*Golem effect*
- The Golem effect is the opposite of the Pygmalion effect, where lower expectations placed upon individuals lead to poorer performance from them.
- In this case, the staff member's influence created **high expectations and positive outcomes**, not negative expectations leading to worse outcomes.
Case-control studies US Medical PG Question 2: A study is performed to determine the prevalence of a particular rare fungal pneumonia. A sample population of 100 subjects is monitored for 4 months. Every month, the entire population is screened and the number of new cases is recorded for the group. The data from the study are given in the table below:
Time point New cases of fungal pneumonia
t = 0 months 10
t = 1 months 4
t = 2 months 2
t = 3 months 5
t = 4 months 4
Which of the following is correct regarding the prevalence of this rare fungal pneumonia in this sample population?
- A. The prevalence at time point 2 months is 2%.
- B. The prevalence at time point 3 months is 11%.
- C. The prevalence at the conclusion of the study is 15%.
- D. The prevalence and the incidence at time point 2 months are equal.
- E. The prevalence at the conclusion of the study is 25%. (Correct Answer)
Case-control studies Explanation: ***The prevalence at the conclusion of the study is 25%***
- Prevalence is calculated by dividing the **total number of existing cases** by the total population at a specific point in time. At the conclusion of the study (t=4 months), the cumulative number of new cases is 10 + 4 + 2 + 5 + 4 = 25.
- The prevalence is therefore 25 cases / 100 subjects = **25%**.
*The prevalence at time point 2 months is 2%*
- At time point 2 months, the **cumulative number of new cases** is 10 (at t=0) + 4 (at t=1) + 2 (at t=2) = 16 cases.
- The prevalence at 2 months would be 16 cases / 100 subjects = **16%**, not 2%.
*The prevalence at time point 3 months is 11%*
- The cumulative number of new cases at time point 3 months is 10 (at t=0) + 4 (at t=1) + 2 (at t=2) + 5 (at t=3) = 21 cases.
- The prevalence at 3 months would be 21 cases / 100 subjects = **21%**, not 11%.
*The prevalence at the conclusion of the study is 15%*
- The cumulative number of new cases at the conclusion of the study (t=4 months) is 10 + 4 + 2 + 5 + 4 = **25 cases**.
- Therefore, the prevalence is 25 cases / 100 subjects = **25%**, not 15%.
*The prevalence and the incidence at time point 2 months are equal*
- **Incidence** refers to the number of *new* cases within a specified period, which at t=2 months is 2 cases.
- **Prevalence** at t=2 months is the cumulative number of cases (10+4+2 = 16 cases), so incidence (2%) and prevalence (16%) are **not equal**.
Case-control studies US Medical PG Question 3: Group of 100 medical students took an end of the year exam. The mean score on the exam was 70%, with a standard deviation of 25%. The professor states that a student's score must be within the 95% confidence interval of the mean to pass the exam. Which of the following is the minimum score a student can have to pass the exam?
- A. 45%
- B. 63.75%
- C. 67.5%
- D. 20%
- E. 65% (Correct Answer)
Case-control studies Explanation: ***65%***
- To find the **95% confidence interval (CI) of the mean**, we use the formula: Mean ± (Z-score × Standard Error). For a 95% CI, the Z-score is approximately **1.96**.
- The **Standard Error (SE)** is calculated as SD/√n, where n is the sample size (100 students). So, SE = 25%/√100 = 25%/10 = **2.5%**.
- The 95% CI is 70% ± (1.96 × 2.5%) = 70% ± 4.9%. The lower bound is 70% - 4.9% = **65.1%**, which rounds to **65%** as the minimum passing score.
*45%*
- This value is significantly lower than the calculated lower bound of the 95% confidence interval (approximately 65.1%).
- It would represent a score far outside the defined passing range.
*63.75%*
- This value falls below the calculated lower bound of the 95% confidence interval (approximately 65.1%).
- While close, this score would not meet the professor's criterion for passing.
*67.5%*
- This value is within the 95% confidence interval (65.1% to 74.9%) but is **not the minimum score**.
- Lower scores within the interval would still qualify as passing.
*20%*
- This score is extremely low and falls significantly outside the 95% confidence interval for a mean of 70%.
- It would indicate performance far below the defined passing threshold.
Case-control studies US Medical PG Question 4: 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
Case-control studies 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.
Case-control studies US Medical PG Question 5: You are reading through a recent article that reports significant decreases in all-cause mortality for patients with malignant melanoma following treatment with a novel biological infusion. Which of the following choices refers to the probability that a study will find a statistically significant difference when one truly does exist?
- A. Type II error
- B. Type I error
- C. Confidence interval
- D. p-value
- E. Power (Correct Answer)
Case-control studies Explanation: ***Power***
- **Power** is the probability that a study will correctly reject the null hypothesis when it is, in fact, false (i.e., will find a statistically significant difference when one truly exists).
- A study with high power minimizes the risk of a **Type II error** (failing to detect a real effect).
*Type II error*
- A **Type II error** (or **beta error**) occurs when a study fails to reject a false null hypothesis, meaning it concludes there is no significant difference when one actually exists.
- This is the **opposite** of what the question describes, which asks for the probability of *finding* a difference.
*Type I error*
- A **Type I error** (or **alpha error**) occurs when a study incorrectly rejects a true null hypothesis, concluding there is a significant difference when one does not actually exist.
- This relates to the **p-value** and the level of statistical significance (e.g., p < 0.05).
*Confidence interval*
- A **confidence interval** provides a range of values within which the true population parameter is likely to lie with a certain degree of confidence (e.g., 95%).
- It does not directly represent the probability of finding a statistically significant difference when one truly exists.
*p-value*
- The **p-value** is the probability of observing data as extreme as, or more extreme than, that obtained in the study, assuming the null hypothesis is true.
- It is used to determine statistical significance, but it is not the probability of detecting a true effect.
Case-control studies US Medical PG Question 6: Which of the following study designs would be most appropriate to investigate the association between electronic cigarette use and the subsequent development of lung cancer?
- A. Subjects with lung cancer who smoke and subjects with lung cancer who did not smoke
- B. Subjects who smoke electronic cigarettes and subjects who smoke normal cigarettes
- C. Subjects with lung cancer who smoke and subjects without lung cancer who smoke
- D. Subjects with lung cancer and subjects without lung cancer
- E. Subjects who smoke electronic cigarettes and subjects who do not smoke (Correct Answer)
Case-control studies Explanation: ***Subjects who smoke electronic cigarettes and subjects who do not smoke***
- This design represents a **cohort study**, which is ideal for investigating the **incidence** of a disease (lung cancer) in groups exposed and unexposed to a risk factor (electronic cigarette use).
- By following these two groups over time, researchers can directly compare the **risk of developing lung cancer** in e-cigarette users versus non-smokers.
*Subjects with lung cancer who smoke and subjects with lung cancer who did not smoke*
- This option incorrectly compares two groups both with lung cancer, where the exposure to smoking can either be **electronic or traditional cigarettes,** but does not provide a control group without lung cancer to assess the association.
- This design would not allow for the calculation of an **incidence rate** or a **relative risk** of lung cancer development specific to electronic cigarette use.
*Subjects who smoke electronic cigarettes and subjects who smoke normal cigarettes*
- This design compares two different types of smoking, which might be useful for comparing their relative risks but doesn't include a **non-smoking control group** to establish the absolute association with electronic cigarettes.
- While it could show if e-cigarettes are "safer" than traditional cigarettes, it wouldn't directly answer whether e-cigarettes themselves **cause lung cancer**.
*Subjects with lung cancer who smoke and subjects without lung cancer who smoke*
- This describes a **case-control study** but focuses on smoking in general rather than specifically electronic cigarettes, which is the independent variable of interest.
- While valuable for identifying risk factors, it would need to specifically differentiate between **electronic cigarette smokers** and other smokers to answer the question adequately.
*Subjects with lung cancer and subjects without lung cancer*
- This general description of a **case-control study** is too broad; it does not specify the exposure of interest, which is electronic cigarette use.
- To be relevant, the study would need to gather data on **electronic cigarette use** in both the lung cancer group and the non-lung cancer control group.
Case-control studies US Medical PG Question 7: You submit a paper to a prestigious journal about the effects of coffee consumption on mesothelioma risk. The first reviewer lauds your clinical and scientific acumen, but expresses concern that your study does not have adequate statistical power. Statistical power refers to which of the following?
- A. The probability of detecting an association when no association exists.
- B. The probability of not detecting an association when an association does exist.
- C. The probability of detecting an association when an association does exist. (Correct Answer)
- D. The first derivative of work.
- E. The square root of the variance.
Case-control studies Explanation: ***The probability of detecting an association when an association does exist.***
- **Statistical power** is defined as the probability that a study will correctly reject a false null hypothesis, meaning it will detect a true effect or association if one exists.
- A study with **adequate statistical power** is less likely to miss a real effect.
*The probability of detecting an association when no association exists.*
- This describes a **Type I error** or **false positive**, often represented by **alpha (α)**.
- It is the probability of incorrectly concluding an effect or association exists when, in reality, there is none.
*The probability of not detecting an association when an association does exist.*
- This refers to a **Type II error** or **false negative**, represented by **beta (β)**.
- **Statistical power** is calculated as **1 - β**, so this option describes the complement of power.
*The first derivative of work.*
- The first derivative of work with respect to time represents **power** in physics, which is the rate at which work is done.
- This option is a **distractor** from physics and is unrelated to statistical power in research.
*The square root of the variance.*
- The **square root of the variance** is the **standard deviation**, a measure of the dispersion or spread of data.
- This is a statistical concept but is not the definition of statistical power.
Case-control studies US Medical PG Question 8: A healthy 29-year-old nulligravid woman comes to the physician for genetic counseling prior to conception. Her brother has a disease that has resulted in infertility, a right-sided heart, and frequent sinus and ear infections. No other family members are affected. The intended father has no history of this disease. The population prevalence of this disease is 1 in 40,000. Which of the following best represents the chance that this patient’s offspring will develop her brother's disease?
- A. 25%
- B. 66%
- C. 0.2% (Correct Answer)
- D. 0.7%
- E. 1%
Case-control studies Explanation: ***0.2%***
- The brother's symptoms (infertility, right-sided heart, frequent infections) are characteristic of **Kartagener syndrome**, a form of **primary ciliary dyskinesia (PCD)**, which has an **autosomal recessive** inheritance pattern.
- Since the patient's parents are obligate heterozygotes (carriers), the patient has a 2/3 chance of being a carrier. Given the population prevalence of 1/40,000 for an autosomal recessive disease, the carrier frequency (2pq) is approximately **2 x sqrt(1/40,000) = 2 x 1/200 = 1/100**. The chance of her child inheriting the disease is (2/3 chance of patient being carrier) x (1/100 chance of partner being carrier) x (1/4 chance of affected offspring) = 2/1200 ≈ **0.00166 or 0.166%**, which is closest to 0.2%.
*25%*
- This would be the risk if both parents were known carriers, and it represents the chance of an affected offspring from two heterozygotes.
- In this scenario, the woman's partner's carrier status is unknown and based on population prevalence, making the overall risk much lower.
*66%*
- This is the probability that the patient (the healthy sister of an affected individual with an autosomal recessive disease) is a **carrier**.
- This value alone does not account for the partner's carrier status or the final Mendelian inheritance probability (1/4) for an affected child.
*0.7%*
- This percentage is too high; it might result from incorrect calculation of the population carrier frequency or misapplication of probabilities.
- The correct carrier frequency for the partner is 1/100, which is significantly lower than what would lead to a 0.7% final risk.
*1%*
- This value is also too high and likely results from a miscalculation of either the carrier frequency or the overall probability.
- A 1% chance would suggest a much higher population carrier frequency or a different inheritance scenario.
Case-control studies US Medical PG Question 9: 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)
Case-control studies 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.
Case-control studies US Medical PG Question 10: A scientist is designing a study to determine whether eating a new diet is able to lower blood pressure in a group of patients. In particular, he believes that starting the diet may help decrease peak blood pressures throughout the day. Therefore, he will equip study participants with blood pressure monitors and follow pressure trends over a 24-hour period. He decides that after recruiting subjects, he will start them on either the new diet or a control diet and follow them for 1 month. After this time, he will switch patients onto the other diet and follow them for an additional month. He will analyze the results from the first month against the results from the second month for each patient. This type of study design is best at controlling for which of the following problems with studies?
- A. Hawthorne effect
- B. Recall bias
- C. Confounding (Correct Answer)
- D. Selection bias
- E. Pygmalion effect
Case-control studies Explanation: ***Confounding***
- This **crossover design** (switching patients to the other diet) effectively controls for **confounding variables** by making each patient their own control, ensuring that inherent patient characteristics do not bias the comparison between diets.
- By comparing the effects of both diets within the same individual, individual variability in factors such as genetics, lifestyle, and other co-morbidities are accounted for, reducing their potential as confounders.
*Hawthorne effect*
- The **Hawthorne effect** refers to subjects modifying their behavior in response to being observed, which this study design does not specifically address or eliminate.
- While patients are being monitored, the design aims to compare the diets' effects, not to prevent behavioral changes due to observation itself.
*Recall bias*
- **Recall bias** occurs when participants' memories of past events are inaccurate, often influenced by their current health status or beliefs.
- This study measures **real-time blood pressure** data, not relying on recollection of past exposures or outcomes, thereby mitigating recall bias.
*Selection bias*
- **Selection bias** arises from non-random selection of participants into study groups, leading to systematic differences between groups.
- While patient recruitment could introduce selection bias into the overall study population, the **crossover design** itself helps control for differences between treatment arms because all participants eventually receive both treatments.
*Pygmalion effect*
- The **Pygmalion effect** (or observer-expectancy effect) describes phenomena where higher expectations lead to increased performance, usually from a researcher influencing a subject.
- This effect is not directly addressed by the crossover design; the design focuses on controlling for patient-specific confounders rather than investigator bias in expectations.
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