Interim analyses US Medical PG Practice Questions and MCQs
Practice US Medical PG questions for Interim analyses. These multiple choice questions (MCQs) cover important concepts and help you prepare for your exams.
Interim analyses US Medical PG Question 1: 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)
Interim analyses 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.
Interim analyses US Medical PG Question 2: 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)
Interim analyses 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.
Interim analyses US Medical PG Question 3: A pharmaceutical company conducts a randomized clinical trial to demonstrate that their new anticoagulant drug, Aclotsaban, prevents more thrombotic events following total knee arthroplasty than the current standard of care. A significant number of patients are lost to follow-up, and many fail to complete treatment according to the study arm to which they were assigned. Despite these protocol deviations, the results for the patients who completed the course of Aclotsaban are encouraging. Which of the following analytical approaches is most appropriate for the primary analysis to establish the efficacy of Aclotsaban?
- A. Intention-to-treat analysis (Correct Answer)
- B. Sub-group analysis
- C. Per-protocol analysis
- D. As-treated analysis
- E. Non-inferiority analysis
Interim analyses Explanation: ***Intention-to-treat analysis***
- **Intention-to-treat (ITT) analysis** is the gold standard for the **primary analysis in superiority trials** and includes all patients in the groups to which they were originally randomized, regardless of protocol deviations, loss to follow-up, or treatment discontinuation.
- ITT preserves **randomization balance**, prevents bias from selective dropout (patients may drop out due to adverse effects or lack of efficacy), and provides a **conservative, realistic estimate** of treatment effect in actual clinical practice.
- For **regulatory approval and establishing efficacy**, ITT is the most appropriate primary analysis method even when dropout rates are high, as it maintains the integrity of the randomized comparison.
*Per-protocol analysis*
- **Per-protocol analysis** includes only patients who completed the study exactly as planned without protocol deviations.
- While the encouraging results in completers are noted, per-protocol analysis can **introduce significant bias** by excluding patients who dropped out due to adverse events or lack of efficacy, potentially **overestimating treatment benefit**.
- Per-protocol is typically used as a **secondary/supportive analysis**, not the primary method for establishing superiority.
*As-treated analysis*
- **As-treated analysis** categorizes patients according to the treatment they actually received rather than their randomized assignment.
- This violates the principle of randomization and can introduce **confounding bias**, as actual treatment received may be influenced by prognostic factors.
*Sub-group analysis*
- **Sub-group analysis** evaluates treatment effects within specific patient subsets.
- This is **hypothesis-generating** rather than confirmatory and increases the risk of false-positive findings (multiple comparisons problem) unless pre-specified in the protocol.
*Non-inferiority analysis*
- **Non-inferiority analysis** tests whether a new treatment is not worse than control by more than a pre-specified margin.
- The goal here is to demonstrate **superiority** (better than standard care), not non-inferiority, making this approach inappropriate.
Interim analyses 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
Interim analyses 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.
Interim analyses 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)
Interim analyses 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.
Interim analyses US Medical PG Question 6: A 28-year-old woman dies shortly after receiving a blood transfusion. Autopsy reveals widespread intravascular hemolysis and acute renal failure. Investigation reveals that she received type A blood, but her medical record indicates she was type O. In a malpractice lawsuit, which of the following elements must be proven?
- A. Duty, breach, causation, and damages (Correct Answer)
- B. Only duty and breach
- C. Only breach and causation
- D. Duty, breach, and damages
Interim analyses Explanation: ***Duty, breach, causation, and damages***
- In a medical malpractice lawsuit, all four elements—**duty, breach, causation, and damages**—must be proven for a successful claim.
- The healthcare provider had a **duty** to provide competent care, they **breached** that duty by administering the wrong blood type, this breach **caused** the patient's death and renal failure, and these injuries constitute **damages**.
*Only duty and breach*
- While **duty** and **breach** are necessary components, proving only these two is insufficient for a malpractice claim.
- It must also be demonstrated that the breach directly led to the patient's harm and resulted in legally recognized damages.
*Only breach and causation*
- This option omits the crucial elements of professional **duty** owed to the patient and the resulting **damages**.
- A claim cannot succeed without establishing that a duty existed and that quantifiable harm occurred.
*Duty, breach, and damages*
- This option misses the critical element of **causation**, which links the provider's breach of duty to the patient's injuries.
- Without proving that the breach *caused* the damages, even if a duty was owed and breached, and damages occurred, the claim would fail.
Interim analyses US Medical PG Question 7: An orthopaedic surgeon at a local community hospital has noticed that turnover times in the operating room have been unnecessarily long. She believes that the long wait times may be due to inefficient communication between the surgical nursing staff, the staff in the pre-operative area, and the staff in the post-operative receiving area. She believes a secure communication mobile phone app would help to streamline communication between providers and improve efficiency in turnover times. Which of the following methods is most appropriate to evaluate the impact of this intervention in the clinical setting?
- A. Plan-Do-Study-Act cycle (Correct Answer)
- B. Failure modes and effects analysis
- C. Standardization
- D. Forcing function
- E. Root cause analysis
Interim analyses Explanation: ***Plan-Do-Study-Act cycle***
- The **Plan-Do-Study-Act (PDSA) cycle** is a structured, iterative model used for continuous improvement in quality and efficiency, making it ideal for evaluating the impact of a new intervention like a communication app.
- This cycle allows for small-scale testing of changes, observation of results, learning from the observations, and refinement of the intervention before full implementation.
*Failure modes and effects analysis*
- **Failure modes and effects analysis (FMEA)** is a prospective method to identify potential failures in a process, predict their effects, and prioritize actions to prevent them.
- While useful for process improvement, FMEA is typically performed *before* implementing a change to identify risks, rather than to evaluate the impact of an already implemented intervention.
*Standardization*
- **Standardization** involves creating and implementing consistent processes or protocols to reduce variability and improve reliability.
- While the communication app might contribute to standardization, standardization itself is a *method of improvement* rather than a method for *evaluating the impact* of an intervention.
*Forcing function*
- A **forcing function** is a design feature that physically prevents an error from occurring, making it impossible to complete a task incorrectly.
- An app that streamlines communication does not act as a forcing function, as it facilitates a process rather than physically preventing an incorrect action.
*Root cause analysis*
- **Root cause analysis (RCA)** is a retrospective method used to investigate an event that has already occurred (e.g., an adverse event) to identify its underlying causes.
- This method is used *after* a problem has manifested to understand *why* it happened, not to evaluate the *impact* of a new intervention designed to prevent future problems.
Interim analyses US Medical PG Question 8: A pharmaceutical company conducts a randomized clinical trial in an attempt to show that their new anticoagulant drug prevents more thrombotic events following total knee arthroplasty than the current standard of care. However, a significant number of patients are lost to follow-up or fail to complete treatment according to the study arm to which they were assigned. Several patients in the novel drug arm are also switched at a later time to a novel anticoagulant or warfarin per their primary care physician. All patients enrolled in the study are subsequently analyzed based on the initial group they were assigned to and there is a significant improvement in outcome of the new drug. What analysis most appropriately describes this trial?
- A. Per protocol
- B. As treated
- C. Non-inferiority
- D. Intention to treat (Correct Answer)
- E. Modified intention to treat
Interim analyses Explanation: ***Intention to treat***
- **Intention-to-treat (ITT)** analysis includes all participants randomized to a treatment arm, regardless of whether they completed the intervention or switched treatments, reflecting a real-world scenario and preserving randomization benefits.
- This approach minimizes bias from **loss to follow-up** or **treatment crossovers** and provides a more conservative estimate of treatment effect.
*Per protocol*
- **Per-protocol analysis** only includes participants who completed the study exactly as planned without any deviations.
- This method is susceptible to **selection bias** because it excludes patients who may have experienced adverse events or treatment failures, potentially overestimating treatment efficacy.
*As treated*
- **As-treated analysis** analyzes patients based on the actual treatment received, rather than the treatment to which they were randomized.
- This approach can introduce **confounding** and selection bias, as patients who switch treatments may do so for reasons related to their prognosis or treatment response.
*Non-inferiority*
- A **non-inferiority trial** design aims to show that a new treatment is not appreciably worse than an active control, rather than proving superiority.
- This describes a **type of study design** or hypothesis, not an analysis method for handling patient data after randomization with non-adherence.
*Modified intention to treat*
- A **modified intention-to-treat (mITT)** analysis typically excludes a small, predefined group of patients from the ITT population, such as those who never received any study drug or were found to be ineligible after randomization.
- While similar to ITT, it involves specific exclusions that are not described in this scenario, where all randomized patients were analyzed **based on initial assignment**.
Interim analyses US Medical PG Question 9: 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
Interim analyses 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.
Interim analyses US Medical PG Question 10: A study seeks to investigate the therapeutic efficacy of treating asymptomatic subclinical hypothyroidism in preventing symptoms of hypothyroidism. The investigators found 300 asymptomatic patients with subclinical hypothyroidism, defined as serum thyroid-stimulating hormone (TSH) of 5 to 10 μU/mL with normal serum thyroxine (T4) levels. The patients were randomized to either thyroxine 75 μg daily or placebo. Both investigators and study subjects were blinded. Baseline patient characteristics were distributed similarly in the treatment and control group (p > 0.05). Participants' serum T4 and TSH levels and subjective quality of life were evaluated at a 3-week follow-up. No difference was found between the treatment and placebo groups. Which of the following is the most likely explanation for the results of this study?
- A. Observer effect
- B. Berkson bias
- C. Latency period (Correct Answer)
- D. Confounding bias
- E. Lead-time bias
Interim analyses Explanation: ***Latency period***
- A **latency period** refers to the time between exposure to a cause (e.g., treatment) and the manifestation of its effects (e.g., symptom improvement). The study's **3-week follow-up is too short** to observe the therapeutic benefits of thyroxine in subclinical hypothyroidism.
- Levothyroxine (T4) has a **half-life of approximately 7 days**, and it typically takes **6-8 weeks or longer** for steady-state levels to be achieved and for clinical symptoms to improve. The slow onset of action for thyroid hormone replacement and the gradual nature of symptom resolution mean a longer observation period (typically 3-6 months) is needed to assess efficacy in hypothyroidism.
- The null results likely reflect insufficient follow-up time rather than lack of treatment effect.
*Observer effect*
- The **observer effect**, or Hawthorne effect, occurs when subjects change their behavior because they know they are being observed. This study used **double-blinding** (both investigators and subjects), which effectively minimizes the observer effect.
- The primary issue here is the lack of observed therapeutic effect due to timing, not a change in behavior due to observation.
*Berkson bias*
- **Berkson bias** is a form of selection bias that arises in case-control studies conducted in hospitals, where the probability of being admitted to the hospital can be affected by both exposure and disease.
- This study is a **randomized controlled trial**, not a case-control study, and the selection of participants does not illustrate this specific bias.
*Confounding bias*
- **Confounding bias** occurs when an extraneous variable is associated with both the exposure and the outcome, distorting the observed relationship. The study states that **baseline patient characteristics were similarly distributed (p > 0.05)**, indicating successful randomization and minimization of confounding.
- While confounding is a common concern in observational studies, the RCT design and reported baseline similarities make it unlikely to be the primary explanation for the null results compared to an insufficient follow-up period.
*Lead-time bias*
- **Lead-time bias** is a form of detection bias where early detection of a disease through screening appears to prolong survival, even if the treatment does not change the course of the disease.
- This study is evaluating the **efficacy of treatment** in asymptomatic individuals with subclinical hypothyroidism, not the effect of screening on survival, making lead-time bias irrelevant to these results.
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