Cross-sectional studies US Medical PG Practice Questions and MCQs
Practice US Medical PG questions for Cross-sectional studies. These multiple choice questions (MCQs) cover important concepts and help you prepare for your exams.
Cross-sectional studies US Medical PG Question 1: A study is funded by the tobacco industry to examine the association between smoking and lung cancer. They design a study with a prospective cohort of 1,000 smokers between the ages of 20-30. The length of the study is five years. After the study period ends, they conclude that there is no relationship between smoking and lung cancer. Which of the following study features is the most likely reason for the failure of the study to note an association between tobacco use and cancer?
- A. Late-look bias
- B. Latency period (Correct Answer)
- C. Confounding
- D. Effect modification
- E. Pygmalion effect
Cross-sectional studies Explanation: ***Latency period***
- **Lung cancer** typically has a **long latency period**, often **20-30+ years**, between initial exposure to tobacco carcinogens and the development of clinically detectable disease.
- A **five-year study duration** in young smokers (ages 20-30) is **far too short** to observe the development of lung cancer, which explains the false negative finding.
- This represents a **fundamental flaw in study design** rather than a bias—the biological timeline of disease development was not adequately considered.
*Late-look bias*
- **Late-look bias** occurs when a study enrolls participants who have already survived the early high-risk period of a disease, leading to **underestimation of true mortality or incidence**.
- Also called **survival bias**, it involves studying a population that has already been "selected" by survival.
- This is not applicable here, as the study simply ended before sufficient time elapsed for disease to develop.
*Confounding*
- **Confounding** occurs when a third variable is associated with both the exposure and outcome, distorting the apparent relationship between them.
- While confounding can affect study results, it would not completely eliminate the detection of a strong, well-established association like smoking and lung cancer in a properly conducted prospective cohort study.
- The issue here is temporal (insufficient follow-up time), not the presence of an unmeasured confounder.
*Effect modification*
- **Effect modification** (also called interaction) occurs when the magnitude of an association between exposure and outcome differs across levels of a third variable.
- This represents a **true biological phenomenon**, not a study design flaw or bias.
- It would not explain the complete failure to detect any association.
*Pygmalion effect*
- The **Pygmalion effect** (observer-expectancy effect) refers to a psychological phenomenon where higher expectations lead to improved performance in the observed subjects.
- This concept is relevant to **behavioral and educational research**, not to objective epidemiological studies of disease incidence.
- It has no relevance to the biological relationship between carcinogen exposure and cancer development.
Cross-sectional studies US Medical PG Question 2: A research team develops a new monoclonal antibody checkpoint inhibitor for advanced melanoma that has shown promise in animal studies as well as high efficacy and low toxicity in early phase human clinical trials. The research team would now like to compare this drug to existing standard of care immunotherapy for advanced melanoma. The research team decides to conduct a non-randomized study where the novel drug will be offered to patients who are deemed to be at risk for toxicity with the current standard of care immunotherapy, while patients without such risk factors will receive the standard treatment. Which of the following best describes the level of evidence that this study can offer?
- A. Level 1
- B. Level 3 (Correct Answer)
- C. Level 5
- D. Level 4
- E. Level 2
Cross-sectional studies Explanation: ***Level 3***
- A **non-randomized controlled trial** like the one described, where patient assignment to treatment groups is based on specific characteristics (risk of toxicity), falls into Level 3 evidence.
- This level typically includes **non-randomized controlled trials** and **well-designed cohort studies** with comparison groups, which are prone to selection bias and confounding.
- The study compares two treatments but lacks randomization, making it Level 3 evidence.
*Level 1*
- Level 1 evidence is the **highest level of evidence**, derived from **systematic reviews and meta-analyses** of multiple well-designed randomized controlled trials or large, high-quality randomized controlled trials.
- The described study is explicitly stated as non-randomized, ruling out Level 1.
*Level 2*
- Level 2 evidence involves at least one **well-designed randomized controlled trial** (RCT) or **systematic reviews** of randomized trials.
- The current study is *non-randomized*, which means it cannot be classified as Level 2 evidence, as randomization is a key criterion for this level.
*Level 4*
- Level 4 evidence includes **case series**, **case-control studies**, and **poorly designed cohort or case-control studies**.
- While the study is non-randomized, it is a controlled comparative trial rather than a case series or retrospective case-control study, placing it at Level 3.
*Level 5*
- Level 5 evidence is the **lowest level of evidence**, typically consisting of **expert opinion** without explicit critical appraisal, or based on physiology, bench research, or animal studies.
- While the drug was initially tested in animal studies, the current human comparative study offers a higher level of evidence than expert opinion or preclinical data.
Cross-sectional studies US Medical PG Question 3: A neuro-oncology investigator has recently conducted a randomized controlled trial in which the addition of a novel alkylating agent to radiotherapy was found to prolong survival in comparison to radiotherapy alone (HR = 0.7, p < 0.01). A number of surviving participants who took the alkylating agent reported that they had experienced significant nausea from the medication. The investigator surveyed all participants in both the treatment and the control group on their nausea symptoms by self-report rated mild, moderate, or severe. The investigator subsequently compared the two treatment groups with regards to nausea level.
| | Mild nausea | Moderate nausea | Severe nausea |
|---|---|---|---|
| Treatment group (%) | 20 | 30 | 50 |
| Control group (%) | 35 | 35 | 30 |
Which of the following statistical methods would be most appropriate to assess the statistical significance of these results?
- A. Chi-square test (Correct Answer)
- B. Pearson correlation coefficient
- C. Multiple logistic regression
- D. Unpaired t-test
- E. Paired t-test
Cross-sectional studies Explanation: **Chi-square test**
- The **Chi-square test** is appropriate for comparing **categorical data** (mild, moderate, severe) between two or more independent groups (treatment vs. control).
- It assesses whether there is a statistically significant association between the two categorical variables (treatment group and nausea severity).
*Pearson correlation coefficient*
- The **Pearson correlation coefficient** is used to measure the **linear relationship** between two **continuous variables**.
- Nausea severity (mild, moderate, severe) is an **ordinal categorical variable**, not a continuous one.
*Multiple logistic regression*
- **Multiple logistic regression** is used to predict a **binary outcome** (e.g., presence or absence of nausea) based on one or more independent variables, which can be continuous or categorical.
- The outcome here is **ordinal categorical** (mild, moderate, severe nausea), not binary. While logistic regression can be adapted for ordinal outcomes, a simpler Chi-square test is more direct for comparing distributions without prediction.
*Unpaired t-test*
- An **unpaired t-test** is used to compare the **means of two independent continuous variables**.
- Nausea levels are categorical, and we are interested in comparing proportions within categories, not means.
*Paired t-test*
- A **paired t-test** is used to compare the **means of two related (paired) continuous variables**.
- The study involves independent treatment and control groups, and the nausea data is categorical, making the paired t-test unsuitable.
Cross-sectional studies US Medical PG Question 4: Study X examined the relationship between coffee consumption and lung cancer. The authors of Study X retrospectively reviewed patients' reported coffee consumption and found that drinking greater than 6 cups of coffee per day was associated with an increased risk of developing lung cancer. However, Study X was criticized by the authors of Study Y. Study Y showed that increased coffee consumption was associated with smoking. What type of bias affected Study X, and what study design is geared to reduce the chance of that bias?
- A. Observer bias; double blind analysis
- B. Selection bias; randomization
- C. Lead time bias; placebo
- D. Measurement bias; blinding
- E. Confounding; randomization (Correct Answer)
Cross-sectional studies Explanation: ***Confounding; randomization***
- Study Y suggests that **smoking** is a **confounding variable** because it is associated with both increased coffee consumption (exposure) and increased risk of lung cancer (outcome), distorting the apparent relationship between coffee and lung cancer.
- **Randomization** in experimental studies (such as randomized controlled trials) helps reduce confounding by ensuring that known and unknown confounding factors are evenly distributed among study groups.
- In observational studies where randomization is not possible, confounding can be addressed through **stratification**, **matching**, or **multivariable adjustment** during analysis.
*Observer bias; double blind analysis*
- **Observer bias** occurs when researchers' beliefs or expectations influence the study outcome, which is not the primary issue described here regarding the relationship between coffee, smoking, and lung cancer.
- **Double-blind analysis** is a method to mitigate observer bias by ensuring neither participants nor researchers know who is in the control or experimental groups.
*Selection bias; randomization*
- **Selection bias** happens when the study population is not representative of the target population, leading to inaccurate results, which is not directly indicated by the interaction between coffee and smoking.
- While **randomization** is used to reduce selection bias by creating comparable groups, the core problem identified in Study X is confounding, not flawed participant selection.
*Lead time bias; placebo*
- **Lead time bias** occurs in screening programs when early detection without improved outcomes makes survival appear longer, an issue unrelated to the described association between coffee, smoking, and lung cancer.
- A **placebo** is an inactive treatment used in clinical trials to control for psychological effects, and its relevance here is limited to treatment intervention studies.
*Measurement bias; blinding*
- **Measurement bias** arises from systematic errors in data collection, such as inaccurate patient reporting of coffee consumption, but the main criticism from Study Y points to a third variable (smoking) affecting the association, not just flawed measurement.
- **Blinding** helps reduce measurement bias by preventing participants or researchers from knowing group assignments, thus minimizing conscious or unconscious influences on data collection.
Cross-sectional studies US Medical PG Question 5: A medical research study is beginning to evaluate the positive predictive value of a novel blood test for non-Hodgkin’s lymphoma. The diagnostic arm contains 700 patients with NHL, of which 400 tested positive for the novel blood test. In the control arm, 700 age-matched control patients are enrolled and 0 are found positive for the novel test. What is the PPV of this test?
- A. 400 / (400 + 0) (Correct Answer)
- B. 700 / (700 + 300)
- C. 400 / (400 + 300)
- D. 700 / (700 + 0)
- E. 700 / (400 + 400)
Cross-sectional studies Explanation: ***400 / (400 + 0) = 1.0 or 100%***
- The **positive predictive value (PPV)** is calculated as **True Positives / (True Positives + False Positives)**.
- In this scenario, **True Positives (TP)** are the 400 patients with NHL who tested positive, and **False Positives (FP)** are 0, as no control patients tested positive.
- This gives a PPV of 400/400 = **1.0 or 100%**, indicating that all patients who tested positive actually had the disease.
*700 / (700 + 300)*
- This calculation does not align with the formula for PPV based on the given data.
- The denominator `(700+300)` suggests an incorrect combination of various patient groups.
*400 / (400 + 300)*
- The denominator `(400+300)` incorrectly includes 300, which is the number of **False Negatives** (patients with NHL who tested negative), not False Positives.
- PPV focuses on the proportion of true positives among all positive tests, not all diseased individuals.
*700 / (700 + 0)*
- This calculation incorrectly uses the total number of patients with NHL (700) as the numerator, rather than the number of positive test results in that group.
- The numerator should be the **True Positives** (400), not the total number of diseased individuals.
*700 / (400 + 400)*
- This calculation uses incorrect values for both the numerator and denominator, not corresponding to the PPV formula.
- The numerator 700 represents the total number of patients with the disease, not those who tested positive, and the denominator incorrectly sums up values that don't represent the proper PPV calculation.
Cross-sectional studies US Medical PG Question 6: A group of environmental health scientists recently performed a nationwide cross-sectional study that investigated the risk of head and neck cancers in patients with a history of cigar and pipe smoking. In collaboration with three teams of epidemiologists that have each conducted similar cross-sectional studies in their respective countries, they have agreed to contribute their data to an international pooled analysis of the relationship between non-cigarette tobacco consumption and prevalence of head and neck cancers. Which of the following statements regarding the pooled analysis in comparison to the individual studies is true?
- A. The results are less precise.
- B. It overcomes limitations in the quality of individual studies.
- C. It is able to provide evidence of causality.
- D. The level of clinical evidence is lower.
- E. The likelihood of type II errors is decreased. (Correct Answer)
Cross-sectional studies Explanation: ***The likelihood of type II errors is decreased.***
- A pooled analysis or **meta-analysis** combines data from multiple studies, significantly increasing the **overall sample size**.
- A larger sample size enhances the statistical power, making it less likely to miss a real effect and thus reducing the probability of **Type II errors** (false negatives).
*The results are less precise.*
- Combining data from multiple studies in a **pooled analysis** generally leads to **more precise estimates** due to the larger sample size and increased statistical power.
- Increased precision is reflected in narrower confidence intervals, offering a more reliable estimate of the effect.
*It overcomes limitations in the quality of individual studies.*
- A pooled analysis **does not inherently overcome limitations** in the design, methodology, or quality of the individual studies included.
- If the original studies have significant biases or flaws, these limitations can be propagated or even amplified in the pooled results.
*It is able to provide evidence of causality.*
- Pooled analyses of **cross-sectional studies**, like the ones described, can identify **associations** but cannot establish **causality**.
- Cross-sectional studies measure exposure and outcome simultaneously, making it impossible to determine the temporal sequence necessary to infer cause and effect.
*The level of clinical evidence is lower.*
- Combining multiple studies, especially well-conducted ones, in a pooled analysis or **meta-analysis** generally **increases the level of clinical evidence**, placing it higher than individual observational studies.
- This is because a pooled analysis offers a more robust and comprehensive view of the existing evidence.
Cross-sectional studies US Medical PG Question 7: A study is conducted to find an association between serum cholesterol and ischemic heart disease. Data is collected, and patients are classified into either the "high cholesterol" or "normal cholesterol" group and also into groups whether or not the patient experiences stable angina. Which type of data analysis is most appropriate for this study?
- A. Attributable risk
- B. Analysis of variance
- C. Chi-squared (Correct Answer)
- D. T-test
- E. Pearson correlation
Cross-sectional studies Explanation: ***Chi-squared***
- The **chi-squared test** is ideal for analyzing two **categorical variables**, such as cholesterol levels (high/normal) and the presence of stable angina (yes/no), to see if there's an association between them.
- It assesses whether the observed frequencies in each category differ significantly from the expected frequencies, under the assumption of no association.
*Attributable risk*
- **Attributable risk** quantifies the proportion of disease in an exposed group that is directly due to the exposure.
- While it might be calculated *after* establishing an association (e.g., using a chi-squared test), it's a measure of actual impact rather than a method for *finding the association* between two categorical variables.
*Analysis of variance*
- **Analysis of variance (ANOVA)** is used to compare the means of **three or more groups** for a continuous outcome variable.
- It works when you have a categorical independent variable with multiple levels and a continuous dependent variable, which is not the case here as both variables are categorical.
*T-test*
- A **t-test** is used to compare the means of **two groups** for a continuous outcome variable.
- It is not appropriate for analyzing the association between two categorical variables like cholesterol categories and angina presence.
*Pearson correlation*
- **Pearson correlation** measures the linear relationship between **two continuous variables**.
- It is unsuitable for this study as both cholesterol status and angina presence are categorical variables, not continuous.
Cross-sectional 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%
Cross-sectional 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.
Cross-sectional studies US Medical PG Question 9: You are currently employed as a clinical researcher working on clinical trials of a new drug to be used for the treatment of Parkinson's disease. Currently, you have already determined the safe clinical dose of the drug in a healthy patient. You are in the phase of drug development where the drug is studied in patients with the target disease to determine its efficacy. Which of the following phases is this new drug currently in?
- A. Phase 4
- B. Phase 1
- C. Phase 2 (Correct Answer)
- D. Phase 0
- E. Phase 3
Cross-sectional studies Explanation: ***Phase 2***
- **Phase 2 trials** involve studying the drug in patients with the target disease to assess its **efficacy** and further evaluate safety, typically involving a few hundred patients.
- The question describes a stage after safe dosing in healthy patients (Phase 1) and before large-scale efficacy confirmation (Phase 3), focusing on efficacy in the target population.
*Phase 4*
- **Phase 4 trials** occur **after a drug has been approved** and marketed, monitoring long-term effects, optimal use, and rare side effects in a diverse patient population.
- This phase is conducted post-market approval, whereas the question describes a drug still in development prior to approval.
*Phase 1*
- **Phase 1 trials** primarily focus on determining the **safety and dosage** of a new drug in a **small group of healthy volunteers** (or sometimes patients with advanced disease if the drug is highly toxic).
- The question states that the safe clinical dose in a healthy patient has already been determined, indicating that Phase 1 has been completed.
*Phase 0*
- **Phase 0 trials** are exploratory, very early-stage studies designed to confirm that the drug reaches the target and acts as intended, typically involving a very small number of doses and participants.
- These trials are conducted much earlier in the development process, preceding the determination of safe clinical doses and large-scale efficacy studies.
*Phase 3*
- **Phase 3 trials** are large-scale studies involving hundreds to thousands of patients to confirm **efficacy**, monitor side effects, compare it to commonly used treatments, and collect information that will allow the drug to be used safely.
- While Phase 3 does assess efficacy, it follows Phase 2 and is typically conducted on a much larger scale before submitting for regulatory approval.
Cross-sectional 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
Cross-sectional 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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