Loss to follow-up handling — MCQs

10 questions

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 otherwise healthy 67-year-old woman comes to your clinic after being admitted to the hospital for 2 weeks after breaking her hip. She has not regularly seen a physician for the past several years because she has been working hard at her long-time job as a schoolteacher. You wonder if she has not been taking adequate preventative measures to prevent osteoporosis and order the appropriate labs. Although she is recovering from surgery well, she is visibly upset because she is worried that her hospital bill will bankrupt her. Which of the following best describes her Medicare coverage?

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?

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 57-year-old man presents to his oncologist to discuss management of small cell lung cancer. The patient is a lifelong smoker and was diagnosed with cancer 1 week ago. The patient states that the cancer was his fault for smoking and that there is "no hope now." He seems disinterested in discussing the treatment options and making a plan for treatment and followup. The patient says "he does not want any treatment" for his condition. Which of the following is the most appropriate response from the physician?

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 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 researcher wants to determine whether there is an association between CRP values and the risk of MI or cancer. Four relative risk (RR) values were plotted $(0.5,1.5,1.7,1.8)$ with respect to CRP levels. What conclusion can be drawn?

A researcher wants to study the carcinogenic effects of a food additive. From the literature, he finds that 7 different types of cancers have been linked to the consumption of this food additive. He wants to study all 7 possible outcomes. He conducts interviews with people who consume food containing these additives and people who do not. He then follows both groups for several years to see if they develop any of these 7 cancers or any other health outcomes. Which of the following study models best represents this study?

Q10

A 52-year-old man comes to the physician for a follow-up examination 1 year after an uncomplicated liver transplantation. He feels well but wants to know how long he can expect his donor graft to function. The physician informs him that the odds of graft survival are 90% at 1 year, 78% at 5 years, and 64% at 10 years. At this time, given that the graft has already survived 1 year, the probability of the patient's graft surviving to 10 years after transplantation is closest to which of the following?

Loss to follow-up handling US Medical PG Practice Questions and MCQs

Question 1: 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 2: An otherwise healthy 67-year-old woman comes to your clinic after being admitted to the hospital for 2 weeks after breaking her hip. She has not regularly seen a physician for the past several years because she has been working hard at her long-time job as a schoolteacher. You wonder if she has not been taking adequate preventative measures to prevent osteoporosis and order the appropriate labs. Although she is recovering from surgery well, she is visibly upset because she is worried that her hospital bill will bankrupt her. Which of the following best describes her Medicare coverage?

A. Medicare Part C will cover the majority of drug costs during her inpatient treatment.
B. Medicare Part A will cover the majority of her hospital fees, including inpatient drugs and lab tests. (Correct Answer)
C. Medicare is unlikely to cover the cost of her admission because she has not been paying her premium.
D. Medicare Part B will cover the majority of her hospital fees, including inpatient drugs and lab tests.
E. Medicare Part D will cover the cost of drugs during her inpatient treatment.

Explanation: ***Medicare Part A will cover the majority of her hospital fees, including inpatient drugs and lab tests.*** * **Medicare Part A** is hospital insurance and covers **inpatient hospital stays**, skilled nursing facility care, hospice care, and some home health care. This includes services received during an inpatient stay such as drugs, lab tests, and surgery. * Given her 2-week hospital stay for a broken hip, which resulted in surgery and ongoing recovery, Part A would be the primary payer for the majority of these costs. *Medicare Part C will cover the majority of drug costs during her inpatient treatment.* * **Medicare Part C**, also known as **Medicare Advantage**, is an alternative to original Medicare provided by private companies, often including Part A, B, and D benefits. * While Part C plans can cover drug costs, **inpatient drugs** administered during a hospital stay are typically covered under **Part A**, not a separate drug plan, if the patient is using original Medicare. If she has a Part C plan, it would integrate these benefits. *Medicare is unlikely to cover the cost of her admission because she has not been paying her premium.* * Medicare Part A is generally **premium-free** for most individuals who have paid Medicare taxes through their employment for at least 10 years (or 40 quarters). * Given her long career as a schoolteacher, it is highly likely she would qualify for premium-free Part A, making this statement incorrect. *Medicare Part B will cover the majority of her hospital fees, including inpatient drugs and lab tests.* * **Medicare Part B** is medical insurance and covers **doctor's services**, outpatient care, medical supplies, and preventive services. * While it covers some outpatient lab tests and physician services received during an inpatient stay, it does not cover the primary costs of **inpatient hospital fees** or drugs administered during an inpatient stay, which fall under Part A. *Medicare Part D will cover the cost of drugs during her inpatient treatment.* * **Medicare Part D** is prescription drug coverage provided by private companies and covers **outpatient prescription drugs**. * Medications administered to an **inpatient** during a hospital stay (i.e., when she is admitted) are typically covered under **Medicare Part A**, not Part D.

Question 3: 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)

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.

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)

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 5: A 57-year-old man presents to his oncologist to discuss management of small cell lung cancer. The patient is a lifelong smoker and was diagnosed with cancer 1 week ago. The patient states that the cancer was his fault for smoking and that there is "no hope now." He seems disinterested in discussing the treatment options and making a plan for treatment and followup. The patient says "he does not want any treatment" for his condition. Which of the following is the most appropriate response from the physician?

A. "You seem upset at the news of this diagnosis. I want you to go home and discuss this with your loved ones and come back when you feel ready to make a plan together for your care."
B. "It must be tough having received this diagnosis; however, new cancer therapies show increased efficacy and excellent outcomes."
C. "It must be very challenging having received this diagnosis. I want to work with you to create a plan." (Correct Answer)
D. "We are going to need to treat your lung cancer. I am here to help you throughout the process."
E. "I respect your decision and we will not administer any treatment. Let me know if I can help in any way."

Explanation: ***"It must be very challenging having received this diagnosis. I want to work with you to create a plan."*** - This response **acknowledges the patient's emotional distress** and feelings of guilt and hopelessness, which is crucial for building rapport and trust. - It also gently **re-engages the patient** by offering a collaborative approach to treatment, demonstrating the physician's commitment to supporting him through the process. *"You seem upset at the news of this diagnosis. I want you to go home and discuss this with your loved ones and come back when you feel ready to make a plan together for your care."* - While acknowledging distress, sending the patient home without further engagement **delays urgent care** for small cell lung cancer, which is aggressive. - This response might be perceived as dismissive of his immediate feelings and can **exacerbate his sense of hopelessness** and isolation. *"It must be tough having received this diagnosis; however, new cancer therapies show increased efficacy and excellent outcomes."* - This statement moves too quickly to treatment efficacy without adequately addressing the patient's current **emotional state and fatalism**. - While factual, it **lacks empathy** for his personal feelings of blame and hopelessness, potentially making him feel unheard. *"We are going to need to treat your lung cancer. I am here to help you throughout the process."* - This response is **too directive and authoritarian**, which can alienate a patient who is already feeling guilty and resistant to treatment. - It fails to acknowledge his stated feelings of "no hope now" or his disinterest in treatment, which are critical to address before discussing the necessity of treatment. *"I respect your decision and we will not administer any treatment. Let me know if I can help in any way."* - While respecting patient autonomy is vital, immediately accepting a patient's decision to refuse treatment without exploring the underlying reasons (e.g., guilt, hopelessness, lack of information) is **premature and potentially harmful**. - The physician has a responsibility to ensure the patient is making an informed decision, especially for a rapidly progressing condition like small cell lung cancer.

Question 6: 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.

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.

Question 7: 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

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**.

Question 8: A researcher wants to determine whether there is an association between CRP values and the risk of MI or cancer. Four relative risk (RR) values were plotted $(0.5,1.5,1.7,1.8)$ with respect to CRP levels. What conclusion can be drawn?

A. CRP has no relationship
B. CRP decreases & disease decreases
C. CRP increases disease/cancer risk (Correct Answer)
D. No association in first interval
E. CRP shows protective effect in first interval

Explanation: ***CRP increases disease/cancer risk*** - A **relative risk (RR)** greater than 1 indicates an increased risk of the outcome (MI or cancer) in the exposed group (higher CRP levels) compared to the unexposed group. - The plots show RRs of 1.5, 1.7, and 1.8, all of which are greater than 1, consistently indicating that higher CRP levels are associated with an elevated risk for MI or cancer. - The overall trend across the four intervals demonstrates a positive association between CRP and disease risk. *CRP has no relationship* - This conclusion is incorrect because three of the four plotted RR values (1.5, 1.7, 1.8) are above 1, indicating a positive association or increased risk. - An RR of 1 signifies no relationship, but the majority of values clearly deviate from 1, showing a definite association. *CRP decreases & disease decreases* - While one RR value (0.5) suggests a decreased risk, the majority of the given RRs (1.5, 1.7, 1.8) are greater than 1, indicating an increased risk. - This option would only be true if all or most RR values were less than 1, implying a protective effect, which is not the overall trend here. *No association in first interval* - The first interval shows an RR of 0.5. An RR of 1 indicates no association, while an RR of 0.5 actually indicates a **decreased risk or protective effect**, rather than no association. - Therefore, stating "no association" for the first interval is inaccurate given the definition of relative risk. *CRP shows protective effect in first interval* - While the first interval RR of 0.5 does suggest a protective effect in isolation, this option fails to capture the **overall conclusion** from all four data points. - When interpreting multiple RR values together, the predominant pattern (three values >1) indicates an overall increased risk, making this a misleading conclusion for the study as a whole.

Question 9: A researcher wants to study the carcinogenic effects of a food additive. From the literature, he finds that 7 different types of cancers have been linked to the consumption of this food additive. He wants to study all 7 possible outcomes. He conducts interviews with people who consume food containing these additives and people who do not. He then follows both groups for several years to see if they develop any of these 7 cancers or any other health outcomes. Which of the following study models best represents this study?

A. Cohort study (Correct Answer)
B. Case-control study
C. Cross-sectional study
D. Randomized clinical trial
E. Crossover study

Explanation: ***Cohort study*** - This study design involves selecting a group based on their **exposure status** (consumers vs. non-consumers of the food additive) and **following them forward in time** to observe the incidence of outcomes (cancers). - It is ideal for studying **multiple potential outcomes** from a single exposure and for establishing the **temporal relationship** between exposure and disease. *Case-control study* - This design starts by identifying individuals with a particular **outcome (cases)** and comparing them to individuals without the outcome (controls) to look back for **past exposures**. - It is efficient for studying **rare diseases** or when multiple exposures are suspected for a single outcome, which is inverse to the scenario described. *Cross-sectional study* - This study measures both **exposure and outcome simultaneously** at a single point in time, providing a snapshot of prevalence. - It cannot establish a **temporal relationship** between exposure and outcome and is less suitable for studying incident diseases that develop over time. *Randomized clinical trial* - This design involves **randomly assigning participants** to an intervention group or a control group and following them for outcomes. - It is primarily used to evaluate the **efficacy of interventions** or treatments, not to study the carcinogenic effects of naturally occurring exposures. *Crossover study* - In a crossover design, participants **receive all interventions** in a specific sequence, making each subject serve as their own control. - This design is generally used for evaluating **short-term effects of treatments** in chronic, stable conditions and is unsuitable for observing the development of diseases like cancer over extended periods.

Question 10: A 52-year-old man comes to the physician for a follow-up examination 1 year after an uncomplicated liver transplantation. He feels well but wants to know how long he can expect his donor graft to function. The physician informs him that the odds of graft survival are 90% at 1 year, 78% at 5 years, and 64% at 10 years. At this time, given that the graft has already survived 1 year, the probability of the patient's graft surviving to 10 years after transplantation is closest to which of the following?

A. 82%
B. 58%
C. 71% (Correct Answer)
D. 64%
E. 45%

Explanation: ***71%*** - This question tests understanding of **conditional probability** in survival analysis. - The patient is currently at 1 year post-transplant with a functioning graft. We need to calculate the probability of surviving to 10 years **given survival to 1 year**. - Using the conditional probability formula: P(survive to 10 years | survived to 1 year) = P(S10) / P(S1) = 64% / 90% = 0.711 ≈ **71%** - This represents the probability that a graft that has already "made it" through the first year will continue functioning until year 10. - In **Kaplan-Meier survival analysis**, conditional probabilities are crucial for counseling patients at different timepoints post-procedure. *64%* - This represents the **absolute probability** of 10-year graft survival measured from the time of transplantation (time zero). - However, the question asks "at this time" (1 year post-transplant) for the conditional probability, not the absolute probability from transplantation. - This would be correct if asking a patient at time zero what their 10-year survival odds are. *82%* - This does not represent any valid calculation from the given survival data. - It may result from incorrect manipulation of the probabilities (e.g., incorrectly adding or averaging values). *58%* - This is not derived from proper statistical calculation of the given survival probabilities. - It does not represent either absolute or conditional probability for any relevant timepoint. *45%* - This is incorrect and does not correspond to any valid calculation. - It might arise from incorrectly multiplying probabilities (e.g., 0.90 × 0.50) but has no basis in survival analysis.

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