Quasi-experimental designs — MCQs

10 questions

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 surgeon is interested in studying how different surgical techniques impact the healing of tendon injuries. In particular, he will compare 3 different types of suture repairs biomechanically in order to determine the maximum load before failure of the tendon 2 weeks after repair. He collects data on maximum load for 90 different repaired tendons from an animal model. Thirty tendons were repaired using each of the different suture techniques. Which of the following statistical measures is most appropriate for analyzing the results of this study?

A researcher is conducting a study to compare fracture risk in male patients above the age of 65 who received annual DEXA screening to peers who did not receive screening. He conducts a randomized controlled trial in 900 patients, with half of participants assigned to each experimental group. The researcher ultimately finds similar rates of fractures in the two groups. He then notices that he had forgotten to include 400 patients in his analysis. Including the additional participants in his analysis would most likely affect the study's results in which of the following ways?

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 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 physician attempts to study cirrhosis in his state. Using a registry of admitted patients over the last 10 years at the local hospital, he isolates all patients who have been diagnosed with cirrhosis. Subsequently, he contacts this group of patients, asking them to complete a survey assessing their prior exposure to alcohol use, intravenous drug abuse, blood transfusions, personal history of cancer, and other medical comorbidities. An identical survey is given to an equal number of patients in the registry who do not carry a prior diagnosis of cirrhosis. Which of the following is the study design utilized by this physician?

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?

An academic medical center in the United States is approached by a pharmaceutical company to run a small clinical trial to test the effectiveness of its new drug, compound X. The company wants to know if the measured hemoglobin a1c (Hba1c) of patients with type 2 diabetes receiving metformin and compound X would be lower than that of control subjects receiving only metformin. After a year of study and data analysis, researchers conclude that the control and treatment groups did not differ significantly in their Hba1c levels. However, parallel clinical trials in several other countries found that compound X led to a significant decrease in Hba1c. Interested in the discrepancy between these findings, the company funded a larger study in the United States, which confirmed that compound X decreased Hba1c levels. After compound X was approved by the FDA, and after several years of use in the general population, outcomes data confirmed that it effectively lowered Hba1c levels and increased overall survival. What term best describes the discrepant findings in the initial clinical trial run by institution A?

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?

Q10

A statistician wants to study the effects of a medicine in three groups-humans, animals, and plants. He then selects randomly from these three groups. Which type of sampling is being performed?

Quasi-experimental designs US Medical PG Practice Questions and MCQs

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

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.

Question 2: A surgeon is interested in studying how different surgical techniques impact the healing of tendon injuries. In particular, he will compare 3 different types of suture repairs biomechanically in order to determine the maximum load before failure of the tendon 2 weeks after repair. He collects data on maximum load for 90 different repaired tendons from an animal model. Thirty tendons were repaired using each of the different suture techniques. Which of the following statistical measures is most appropriate for analyzing the results of this study?

A. Chi-squared
B. Wilcoxon rank sum
C. Pearson r coefficient
D. Student t-test
E. ANOVA (Correct Answer)

Explanation: ***ANOVA*** - **ANOVA (Analysis of Variance)** is appropriate here because it compares the means of **three or more independent groups** (the three different suture techniques) on a continuous dependent variable (maximum load before failure). - The study has three distinct repair techniques, each with 30 tendons, making ANOVA suitable for determining if there are statistically significant differences among their mean failure loads. *Chi-squared* - The **Chi-squared test** is used for analyzing **categorical data** (frequencies or proportions) to determine if there is an association between two nominal variables. - This study involves quantitative measurement (maximum load), not categorical data, making Chi-squared inappropriate. *Wilcoxon rank sum* - The **Wilcoxon rank sum test** (also known as Mann-Whitney U test) is a **non-parametric test** used to compare two independent groups when the data is not normally distributed or is ordinal. - While the study has independent groups, it involves three groups, and the dependent variable is continuous, making ANOVA a more powerful and appropriate choice assuming normal distribution. *Pearson r coefficient* - The **Pearson r coefficient** measures the **strength and direction of a linear relationship between two continuous variables**. - This study aims to compare means across different groups, not to determine the correlation between two continuous variables. *Student t-test* - The **Student t-test** is used to compare the means of **exactly two groups** (either independent or paired) on a continuous dependent variable. - This study involves comparing three different suture techniques, not just two, making the t-test unsuitable.

Question 3: A researcher is conducting a study to compare fracture risk in male patients above the age of 65 who received annual DEXA screening to peers who did not receive screening. He conducts a randomized controlled trial in 900 patients, with half of participants assigned to each experimental group. The researcher ultimately finds similar rates of fractures in the two groups. He then notices that he had forgotten to include 400 patients in his analysis. Including the additional participants in his analysis would most likely affect the study's results in which of the following ways?

A. Wider confidence intervals of results
B. Increased probability of committing a type II error
C. Decreased significance level of results
D. Increased external validity of results
E. Increased probability of rejecting the null hypothesis when it is truly false (Correct Answer)

Explanation: ***Increased probability of rejecting the null hypothesis when it is truly false*** - Including more participants increases the **statistical power** of the study, making it more likely to detect a true effect if one exists. - A higher sample size provides a more precise estimate of the population parameters, leading to a greater ability to **reject a false null hypothesis**. *Wider confidence intervals of results* - A larger sample size generally leads to **narrower confidence intervals**, as it reduces the standard error of the estimate. - Narrower confidence intervals indicate **greater precision** in the estimation of the true population parameter. *Increased probability of committing a type II error* - A **Type II error** (false negative) occurs when a study fails to reject a false null hypothesis. - Increasing the sample size typically **reduces the probability of a Type II error** because it increases statistical power. *Decreased significance level of results* - The **significance level (alpha)** is a pre-determined threshold set by the researcher before the study begins, typically 0.05. - It is independent of sample size and represents the **acceptable probability of committing a Type I error** (false positive). *Increased external validity of results* - **External validity** refers to the generalizability of findings to other populations, settings, or times. - While a larger sample size can enhance the representativeness of the study population, external validity is primarily determined by the **sampling method** and the study's design context, not just sample size alone.

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

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.

Question 5: 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)

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.

Question 6: A physician attempts to study cirrhosis in his state. Using a registry of admitted patients over the last 10 years at the local hospital, he isolates all patients who have been diagnosed with cirrhosis. Subsequently, he contacts this group of patients, asking them to complete a survey assessing their prior exposure to alcohol use, intravenous drug abuse, blood transfusions, personal history of cancer, and other medical comorbidities. An identical survey is given to an equal number of patients in the registry who do not carry a prior diagnosis of cirrhosis. Which of the following is the study design utilized by this physician?

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

Explanation: ***Case-control study*** - This study design **identifies subjects based on their outcome (cases with cirrhosis, controls without cirrhosis)** and then retrospectively investigates their past exposures. - The physician selected patients with cirrhosis (cases) and patients without cirrhosis (controls), then assessed their prior exposures to risk factors like alcohol use and intravenous drug abuse. *Randomized controlled trial* - This design involves randomly assigning participants to an **intervention group** or a **control group** to assess the effect of an intervention. - There is no intervention being tested or randomization occurring in this study; it is observational. *Cross-sectional study* - A cross-sectional study measures the **prevalence of disease and exposure at a single point in time** in a defined population. - This study collects retrospective exposure data and compares two distinct groups (cases and controls), rather than assessing prevalence at one time point. *Cohort study* - A cohort study **follows a group of individuals over time** to see if their exposure to a risk factor is associated with the development of a disease. - This study starts with the outcome (cirrhosis) and looks backward at exposures, which is the opposite direction of a cohort study. *Meta-analysis* - A meta-analysis is a statistical method that **combines the results of multiple independent studies** to produce a single, more powerful estimate of treatment effect or association. - This is an original research study collecting new data, not a systematic review or synthesis of existing studies.

Question 7: 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 8: An academic medical center in the United States is approached by a pharmaceutical company to run a small clinical trial to test the effectiveness of its new drug, compound X. The company wants to know if the measured hemoglobin a1c (Hba1c) of patients with type 2 diabetes receiving metformin and compound X would be lower than that of control subjects receiving only metformin. After a year of study and data analysis, researchers conclude that the control and treatment groups did not differ significantly in their Hba1c levels. However, parallel clinical trials in several other countries found that compound X led to a significant decrease in Hba1c. Interested in the discrepancy between these findings, the company funded a larger study in the United States, which confirmed that compound X decreased Hba1c levels. After compound X was approved by the FDA, and after several years of use in the general population, outcomes data confirmed that it effectively lowered Hba1c levels and increased overall survival. What term best describes the discrepant findings in the initial clinical trial run by institution A?

A. Type I error
B. Hawthorne effect
C. Type II error (Correct Answer)
D. Publication bias
E. Confirmation bias

Explanation: ***Type II error*** - A **Type II error** occurs when a study fails to **reject a false null hypothesis**, meaning it concludes there is no significant difference or effect when one actually exists. - In this case, the initial US trial incorrectly concluded that Compound X had no significant effect on HbA1c, while subsequent larger studies and real-world data proved it did. *Type I error* - A **Type I error** (alpha error) occurs when a study incorrectly **rejects a true null hypothesis**, concluding there is a significant difference or effect when there isn't. - This scenario describes the opposite: the initial study failed to find an effect that genuinely existed, indicating a Type II error, not a Type I error. *Hawthorne effect* - The **Hawthorne effect** is a type of reactivity in which individuals modify an aspect of their behavior in response to their awareness of being observed. - This effect does not explain the initial trial's failure to detect a real drug effect; rather, it relates to participants changing behavior due to study participation itself. *Publication bias* - **Publication bias** occurs when studies with positive or statistically significant results are more likely to be published than those with negative or non-significant results. - While relevant to the literature as a whole, it doesn't explain the discrepancy in findings within a single drug's development where a real effect was initially missed. *Confirmation bias* - **Confirmation bias** is the tendency to search for, interpret, favor, and recall information in a way that confirms one's preexisting beliefs or hypotheses. - This bias would likely lead researchers to *find* an effect if they expected one, or to disregard data that contradicts their beliefs, which is not what happened in the initial trial.

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

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.

Question 10: A statistician wants to study the effects of a medicine in three groups-humans, animals, and plants. He then selects randomly from these three groups. Which type of sampling is being performed?

A. Simple random sampling
B. Systematic sampling
C. Stratified random sampling (Correct Answer)
D. Cluster sampling
E. Convenience sampling

Explanation: ***Stratified random sampling*** - This method involves dividing the population into **distinct subgroups (strata)** based on shared characteristics (in this case, humans, animals, and plants), and then performing a simple random sample within each stratum. - This ensures that all subgroups are proportionally represented in the sample, which is appropriate when studying effects across different biological categories. *Simple random sampling* - This method involves selecting individuals from the entire population **purely by chance**, without first dividing them into subgroups. - It would not guarantee representation from all three distinct groups (humans, animals, and plants), which is essential for studying differential effects. *Systematic sampling* - This involves selecting samples at **regular intervals** from an ordered list or sequence. - This method is not suitable here because the population is divided into distinct, non-ordered groups rather than a continuous sequence. *Cluster sampling* - This method involves dividing the population into **clusters**, then randomly selecting some clusters and sampling all individuals within those selected clusters. - In this scenario, the initial groups (humans, animals, plants) are strata, not clusters, as the intent is to sample from within each group, not to treat the groups themselves as primary sampling units. *Convenience sampling* - This is a **non-probability sampling method** where subjects are selected based on ease of access rather than random selection. - The question explicitly states that random selection is performed from each group, ruling out convenience sampling.

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